The implementation of cybersecurity standards and directives in the maritime sector plays a crucial role in protecting critical maritime infrastructures from cyber threats. The level of protection depends heavily on humans. However, the effectiveness of cybersecurity training and compliance programs, an essential component of these standards, is often hindered by challenges related to the sector’s environment, including the established technologies, practices, and norms. This paper aims to identify these challenges through a literature review and set the basis for more effective human risk minimisation, responses, and training. We identify 18 challenges and validate them with an online survey (N=205) capturing real-world perspectives from maritime-related stakeholders. Our findings contribute to enhancing the effectiveness of maritime cybersecurity training and compliance programs, ultimately strengthening the maritime cybersecurity posture.

International cooperation and food diplomacy play a pivotal role in addressing global challenges to food security. This study explores the effectiveness of Indonesia's international partnerships and diplomatic strategies in bolstering its food security amidst global uncertainties. The research employs a mixed-methods approach, combining qualitative analysis of policy documents and interviews with key stakeholders, alongside quantitative data on food production and trade. Our findings indicate that Indonesia has benefited significantly from technical and financial assistance through collaboration with organizations such as FAO, ASEAN, and bilateral agreements with countries like Japan and China. These partnerships have facilitated the adoption of advanced agricultural technologies, improved infrastructure, and enhanced human resource capacities. However, challenges remain, including bureaucratic inefficiencies and dependency on food imports, which pose risks to sustainable food security. The study concludes that while international cooperation has provided critical support, Indonesia must further streamline its policy implementation processes and reduce its reliance on imports by fostering local production. Strengthening research and development collaborations and actively participating in global forums are also recommended to ensure long-term resilience and food sovereignty. This research provides valuable insights for policymakers and stakeholders aiming to develop effective strategies to navigate the complexities of global food diplomacy and cooperation.

The world is currently experiencing COVID-19, one of the worst pandemics that have happened in this century, affecting 10.7 million people worldwide. It has caused massive growth in the number of employees working from home. However, employees have minimal cybersecurity resources unlike organizations with security teams protecting them against attacks. Hence, cybersecurity plays an important role as users can be easily targeted by cybercriminals. This paper examines how cyberattacks have increased during this pandemic and shows how greatly they have affected health organizations, individuals and social networking applications. Results of the attacks include data breaches, false announcements and operations being disrupted. Attacks occurring during this pandemic and how they were handled are also critically discussed. The existing contributions do touch on related attacks but do not provide in-depth solutions regarding the issues. Even though there are many works and findings that were done previously, technology is ever evolving. Therefore, we need to be well versed with current and future issues and provide the latest mechanisms to prevent cybersecurity threats from occurring. On our share, we intend to present our findings on the challenges being faced by the population and its increasing threats as well as presenting unique solutions that can help organizations or related persons understand or spread awareness on the importance of cybersecurity. Through the research performed in this paper, it is found that there are many ways these issues can be alleviated. However, the issue is that there is significant lack of action and investment in terms of actual implementation and application of the available solutions.

Presently, smartphones support a large range of applications, many of which require high computing power. This presents a problem because smartphones offer limited computing power, storage, and energy. Fortunately, Cloud computing (CC) is rapidly becoming known as the state of the art technology in the computer world. CC allows users to use unlimited dynamic resources when necessary. Mobile Cloud Computing (MCC) is integration into a mobile environment of the concept of cloud computing which eliminates barriers to the performance of mobile devices. The demand for mobile cloud applications and mobile user services is strong. This gives MCC a great opportunity to do business and research. However, security problems are a major obstacle to the adaptability of mobile cloud computing. This study presents the definitions of Cloud Computing, Mobile Computing (MC) and Mobile Cloud Computing including the architecture and applications of MCC. Furthermore, it discusses the challenges and opportunities faced in MCC.

The industrial metaverse paradigm is generally a virtual environment that integrates various technologies such as augmented reality (AR) and mixed reality (MR) to enhance business operations and processes. It aims to streamline workflows, reduce error rates, improve efficiency, and provide a more engaging experience for employees. The promise of the industrial Metaverse to drive sustainability and resource efficiency is compelling. Utilizing advanced technologies like the Industrial Metaverse is essential for staying competitive in today’s rapidly evolving business environment. However, the environmental impact of the technologies underpinning the industrial metaverse, like data centres and network infrastructure, should not be overlooked. The ecological footprint of these technologies must be considered in the sustainability equation. Researchers have warned that by 2025, without sustainable AI practices, AI will consume more energy than the human workforce, significantly offsetting carbon-zero gains. As the metaverse persists in evolving and gaining momentum, it will be necessary for companies to prioritize sustainability and explore new ways to balance technological advancements with environmental stewardship. However, recent studies have conjectured that the Metaverse holds the potential to reduce carbon emissions, as digital replacements for physical goods become more prevalent and physical activities like mobility and construction are reduced. However, the specific extent to which this substitution can alleviate environmental concerns remains understudied, presenting a knowledge gap in understanding the real-world impact of digital replacements. Thus, the objective of this paper is to provide a comprehensive review of the industrial metaverse as well as explore the environmental impact of the industrial metaverse. The methodological approach is that of integrative literature review design wherein multiple sources from web science and databases such as ACM library, IEEE Library, and Google Scholar, were analysed to provide a comprehensive understanding of the developments in the industrial metaverse. Specifically, firstly by considering the Industrial metaverse architecture we elucidate the concept of the "industrial metaverse" and the associated enabling technologies. Secondly, an exploration through discussion, of the prevalent use cases and deployment of the emerging industrial metaverse. Thirdly, exploration of the impact of the industrial metaverse on the environment. In the fourth instance, we address novel security and privacy risks as well as upcoming research challenges, keeping in mind that the industrial metaverse is based on a strong data fabric. Results point to the Industrial Metaverse as having both positive and negative environmental effects by energy consumption, e-waste, and pollution. Overall research, however, indicates that most industrial metaverse applications have a positive environmental impact and subsequently trend toward sustainability. Ultimately for sustainability in the industrial metaverse, enterprises may consider utilizing renewable energy sources and cloud services. Furthermore, examining the effects of products on the environment as well as creating a circular economy.

Immense challenges arise in the Quality Assurance area due to contemporary development in Internet of Things (IoT) technology. Current issues are mainly related to test coverage, test diversity, IoT Stability, Use of Cellular Networks in IoTs, IoT Devices updates, Security, Data Integration, and interoperability. In this paper, we present all those issues with suggestions for tackling those issues.

Ensuring the security of vertical applications in fifth-generation (5G) mobile communication systems and previous generations is crucial. These systems must prioritize maintaining the confidentiality, integrity, and availability of services and data. Examples of vertical applications include smart cities, smart transportation, public services, Industry 4.0, smart grids, smart health, and smart agriculture. Each vertical application has specific security requirements and faces unique threats within the mobile network environment. Thus, it is essential to implement comprehensive and robust security measures. This approach helps minimize the attack surface and effectively manage risks. This survey thoroughly examines mobile networks and their security challenges in vertical applications, shedding light on associated threats and potential solutions. Our study considers the interplay between security considerations in 5G, legacy networks, and vertical applications. We emphasize the challenges, opportunities, and promising directions for future research in this field and the importance of securing vertical applications in the evolving landscape of mobile technology.

Grid modernization has been ushered in by rising electricity consumption, deteriorating infrastructure, and increasing reliability concerns for electric utilities. New advances, collectively referred to as the smart grid, include modern electronics, technology, telecommunications, and computing capabilities. Smart grid telecommunication frameworks provide two-way communication to support grid operations. Software-defined networking (SDN) has been proposed as a method of monitoring and controlling telecommunication networks, enabling increased smart grid visibility, control, and security. However, being connected to telecommunications infrastructure means that smart grid networks are exposed to cyber-attacks. Attackers may use unauthorized access to intercept messages, inject false data into system measurements, flood communication channels with false data packets, or target centralized controllers to cripple network control. Defense and security techniques against these threats are constantly evolving, necessitating an up-to-date, comprehensive study analyzing cyber attacks and defense methods for smart grid networks. Previous smart grid security surveys do not contain recent techniques and to our knowledge most, if not all, address only one type of attack type and/or one type of defense. This survey considers the latest security techniques, simultaneous multi-pronged cyber attacks and defensutilityes to meet the challenges of the next-generation SDN smart grid research with the goal of identifying future research needs, describing the open security challenges, and exposing both emerging threats and their potential impact on SD-SG deployment.

One of the special trends in health care is the increasing availability of data and services to the cloud, especially for convenience (for example, providing a complete patient medical record without interruption) and savings (for example, economic issues). Management of health care data). However, there are limitations to using common cryptographic prototypes and access control models to address security and privacy concerns in an increasingly cloudy environment. In this paper, we explore the potential and capacity of using China's Blockchain technology to protect health care data hosted in the cloud. We also explain the real challenges of such an approach and further research is needed. Health care is a highly data-dependent domain, with large amounts of data being created, published, stored and accessed daily. For example, data are created when a patient undergoes a number of examinations (such as computed tomography or computed tomography scans) and the data need to be sent to the radiologist and then to a physician. The visit results are then stored in the hospital, and then need to be accessed later by another physician at another hospital within the network. It is clear that technology can play an important role in improving the quality of care for patients (for example, using data analytics to make informed medical decisions) and potentially costing more by allocating resources more efficiently in terms of personnel, equipment, etc. , Reduce. For example, paper-based data extraction is difficult to extract into systems (for example, it is costly and may involve data entry errors), archiving them and accessing them as needed is costly. These challenges may cause medical decisions to be incomplete, requiring repeated tests for missing information or missing data stored in another hospital in another state or country (at the expense of increased costs and no convenience). (For patients) and so on. Because of the nature of the industry, it is important to ensure the security, privacy and integrity of health care data. As a result, there is definitely a need for a secure and secure data management system.

In recent years, wireless networks have undoubtedly become a convenient way to connect to the Internet and provide connection to everyone in any corner of the world. In fact, in this era, people are connected to the internet almost everyday and wireless networks give us this privilege in a seamless manner. A wireless network normally consists of access points and nodes where the access points are responsible to amplify the wireless signals, while the nodes are the gadgets that are receiving these signals. However, with such great convenience provided, many challenges are also faced by the users and stakeholders. With no physical connection to devices, wireless networks are evidently more vulnerable to invisible cyber attacks. In this research paper, it the security issues that cause issues in the wireless networks are discussed. Furthermore, an analytical review of privacy challenges found in these networks is performed; these challenges are segregated into security issues and privacy issues. The paper will then present the methods used in conducting a survey and gathering the research results along with further discussion on the results obtained through this study. Finally, a suitable solution is proposed to prevent and overcome the intrusions faced in terms of security and privacy in wireless network scenarios through detection and response mechanism for mitigation of the problems.

The main focus of this paper is to analyze and discuss the secure software development practices currently being adopted in the industry along with their significance, as well as to identify the challenges faced by developers when undertaking measures and techniques in writing secure software. It is a well-known fact that software security has been the top priority of many software companies such as Google and Facebook to thwart attackers and protect user data in this world full of cybercriminals. Understanding how most software companies in the industry operate to ensure security helps developers to identify strengths and weaknesses in their current security frameworks. Hence, by researching into previous literature and papers that are relevant to the topic and by conducting an interview with a professional in the field, this paper provides insights on the most popular secure software development framework and practices in the world as well as problems faced by companies when adopting these practices. Several security practices and activities that are required to create secure software are discovered alongside the problems that arise when companies are trying to apply these practices. This paper also proposes a few solutions that can be used to resolve these problems, which can be easily understood and implemented by software companies to transition into a truly secure software development environment.

The rapid proliferation of Internet of Things (IoT) technology has ushered in a new era of connectivity, allowing billions of devices to produce extensive data. Significant challenges in software engineering need to be addressed for full utilization of Internet of Things (IoT) applications. This article examines the challenges in Internet of Things, software engineering and discusses current developments aimed at addressing these issues and seizing new opportunities. We examine the challenges associated with Internet of Things software development, including resource constraints, device diversity, security vulnerabilities, and data management concerns. New studies have indicated the use of advanced techniques like as containerization, model-driven development, AI and ML integration, and robust security frameworks. Moreover, the significance of system architecture is explained, including the device, network, and cloud layers, with an emphasis on the principles of reusability, scalability, and modularity. Results and conversations explore the complexities of security measures, emerging opportunities, and scaling solutions made possible by simulation tools, penetration testing, and cross-domain development platforms. The study expects IoT system evolution and emphasises the need for continual software and firmware changes, testing, and deployment.

The Internet of Things (IoT) is a disruptive technology innovation that has the potential to link billions of gadgets and alter multiple industries. The Internet of Things (IoT) represents a significant advancement in the networking of devices and systems. However, because IoT devices add new attack avenues and vulnerabilities to networks, their rapid proliferation has also caused serious cyber security concerns. The present study investigates the influence of cyber security on the progress of the Internet of Things (IoT), emphasizing the obstacles, prospects, and future paths towards safeguarding the IoT network. This study attempts to shed light on the crucial role that cyber security will play in determining the direction of the Internet of Things through an examination of recent trends, case studies, and research findings.

This study examines the significant impact of Houthi insurgent activities on maritime traffic within the strategic Red Sea and Suez Canal routes, essential conduits for global trade. It explores the correlation between regional instability, exemplified by Houthi actions from November 19, 2023, to February 5, 2024, and changes in maritime traffic patterns and operational efficiency. This study seeks to answer a critical question in transport geography: How does regional instability, exemplified by Houthi insurgent activities, affect maritime traffic patterns and the operational efficiency of the Red Sea and Suez Canal? Using descriptive statistics, qualitative analysis, and geospatial methods, the research highlights recent trends in maritime traffic and incidents, revealing spatial and geopolitical challenges in this crucial trade route.Findings indicate a notable decline in maritime activity in the Gulf of Aden and Suez Canal due to security concerns from Houthi attacks, prompting a significant shift to alternative routes, particularly around the Cape of Good Hope. This shift underscores the broader implications of regional instability on global trade and the importance of maintaining uninterrupted maritime flow. The study also emphasizes the economic ramifications, such as increased operational costs and freight rates due to longer transit times and enhanced security measures.The research concludes with a call for improved maritime security protocols and international cooperation to protect these strategic maritime pathways. It contributes to the discourse on transport geography by quantifying the direct impacts of regional conflicts on maritime logistics and proposing strategies for future resilience, highlighting the interconnected nature of global trade and security and the need for collective action against evolving geopolitical challenges.

Good health and well-being is one of the essential SDGs that ensure healthy lives and promote well-being for all ages and further entails providing substantial medical services to the public at low cost and with minimal adverse effects on the environment. Information and communication technologies (ICTs) have taken on an increasingly important function as significant facilitators of healthcare reform, with the goals of enhancing access to health services, the quality of treatment provided, and the overall productivity of the healthcare system. However, the integration of ever-increasing ICT technologies into the healthcare systems, also referred to as digital transformation, is not a straightforward process, but it comes with different types of challenges from integration level to application design level and security level. Although several studies have been proposed to address the integration of ICT technologies into healthcare systems, there is still a need for a comprehensive research study on the integration and design challenges, security and privacy challenges, application areas, and possible positive and negative impacts. Therefore, this paper contributes as the research literature study covering an important SDG, "Good health and well-being," and its digital transformation, along with summarising our research findings in a detailed and taxonomical way. To start with, firstly, we present a detailed comparison of existing studies on healthcare and well-being, mainly focusing on integrating ICT technologies in healthcare in terms of sustainable aspects, security and privacy challenges, design and integration challenges, E-health-related applications, and future directions. We also present an overview and the need for digital transformation in healthcare, discuss its significant components, highlight E-health's importance and benefits, explore its integration and design challenges, and categorise the security and privacy challenges. Next, we present an in-depth discussion on the role of Blockchain technology as today's leading technology in E-health, discussing Blockchain technology and its characteristics, highlighting its benefits, and describing the possible types of Blockchain-based E-health use cases. Furthermore, we discuss the positive and negative impact of ICT integration along with identifying open issues and challenges of integrating ICT technologies into the healthcare systems and discuss future research directions, which provide the strength for researchers to address the issues in future solutions.

This paper analyzes ServiceNow’s overall strategy, business model, mission, vision, and key strategic risks. ServiceNow, a platform-as-a-service provider, has experienced significant growth and is poised for further expansion. The paper identifies strategic risks such as legislative and regulatory challenges, strategy development and execution risks, and competition in the cloud computing industry. Using Porter’s Five Forces framework, the assignment explores these risks and proposes mitigation strategies. Recommendations are made to protect intellectual property, maintain customer loyalty through subscription models, and ensure innovation in response to evolving market demands.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

While the financial technology (FinTech) sector has revolutionized conventional banking services with technology-driven solutions, it also faces considerable cybersecurity hurdles to guard against data protection and sustain the overall financial performance. The main purpose of this paper is to identify the key cybersecurity challenges encountered by FinTech companies, such as data confidentiality concerns, complex cyber-attacks, difficulties with regulatory compliance and third-party risks or technological innovations. This also assesses the regulatory responses to those challenges, such as data privacy regulations, macroprudential regimes, industry-specific guidance and international regulatory cooperation. Through this analysis, the paper attempts to synthesize current state of cybersecurity domain within FinTech and provides suggestions for strengthening data shelter and financial stability by implementing best practices with regulatory strategies.

The adoption of the Internet of Things (IoT) in supply chain operations has emerged as a pivotal advancement, offering the potential to significantly enhance operational efficiency, visibility, and responsiveness. This study explores the multifaceted impact of IoT integration in supply chains, emphasizing both the opportunities and challenges associated with its implementation. Through a combination of semi-structured interviews, case studies, and a comprehensive literature review, the research uncovers how IoT technologies contribute to improved asset tracking, real-time inventory management, and optimized logistics. Key benefits include enhanced operational efficiency, predictive maintenance capabilities, and better supply chain collaboration, which collectively lead to increased customer satisfaction and cost savings. However, the study also identifies several challenges, including the high costs of IoT infrastructure, difficulties in integrating new technologies with existing systems, and concerns over data security and privacy. These challenges pose significant barriers to adoption, particularly for smaller organizations. Despite these obstacles, the study highlights the substantial opportunities presented by IoT, such as fostering innovation, developing new business models, and enhancing supply chain resilience. The findings suggest that while the path to successful IoT adoption may be complex and resource-intensive, the potential benefits make it a valuable investment for organizations seeking to remain competitive in a rapidly evolving market. Addressing the identified challenges while leveraging the advantages of IoT will be crucial for companies aiming to optimize their supply chain operations and achieve long-term success.

In the era of Big Data and the Internet of Things (IoT), ensuring the security and privacy of patient data within distributed health systems is paramount. This research delves into the challenges posed by these advancements and explores viable solutions, focusing notably on blockchain technology, data encryption methods, and access control mechanisms. Employing a comprehensive literature review approach, the study synthesizes current research findings, complemented by analyses of existing frameworks and case studies from healthcare organizations, offering practical insights. Interviews with cybersecurity experts and healthcare IT professionals provide nuanced perspectives on current security issues and emerging trends. Through qualitative and quantitative data analyses, the study generates recommendations aimed at enhancing patient data security. These include the adoption of new frameworks and innovative technologies to fortify healthcare data systems. By addressing these critical aspects, this research aims to significantly contribute to the advancement of a more secure and efficient healthcare ecosystem in today's digital landscape.

Crop plants should be resilient to climatic factors in order to feed ever-increasing populations. Plants have developed stress-responsive mechanisms by changing their metabolic pathways and switching the stress-responsive genes. The discovery of plant transcriptional factors (TFs) as key regulators of different biotic and abiotic stresses have opened up new horizons for plant scientists. TFs perceive the signal and switch certain stress-responsive genes on and off by binding to different cis-regulatory elements. The above 50 species of plant TFs have been reported in nature. DREB, bZIP, MYB, NAC, Zinc-finger, HSF, Dof, WRKY, and NF-Y are important with respect to biotic and abiotic stresses whereas the role of many TFs is yet to explore. In this review, we summarize the role of different stress-responsive TFs with respect to biotic and abiotic stresses. Further, challenges and future opportunities linked with TFs for developing climate-resilient crops are also elaborated.

The vast network of interconnected devices of the Internet of Things poses significant security challenges, necessitating the upgrade of current wireless networks to the 6G standard and an improved intrusion detection system for host networks. Thus this article therefore underscores a game theory concept to analyze the security impediments in wireless communication systems, where a host network bandwidth is jeopardized by the intruder while acting as a legitimate user. While game theory can help with the impending security concerns, network slicing is saliently proposed for large and complicated networks, precipitating a layer wise fragmentation of the concerned network and effectively addressing the security issues in different layers of the IoT framework. However, with increasing network complexity, network security issues incorporating traditional measures become increasingly cumbersome. Therefore, this paper also briefly mentions some of the ML techniques that can be used to classify and segregate valid users from the compromised nodes in the host network. The paper discusses the potential of incorporating intelligence into wireless communication systems to enhance the host network security. The paper further aids in analyzing existing security issues in different layers of wireless communication systems, underscoring network slicing for further classification of intruders from user nodes and training the host network using ML/AI per the user application specification.

This study presents a comprehensive systematic review that addresses the critical transition from ROS 1 to ROS 2, spotlighting the significant enhancements and the pressing need for a detailed exploration of ROS 2 within the robotics community. Despite the extensive deployment and adaptations of ROS in varied robotics applications, literature lacks a cohesive synthesis that delineates the advancements, limitations, and broader impacts of ROS 2 compared to its predecessor, ROS 1. Our contribution bridges this gap by assembling the largest database of ROS-related research, encompassing 7,498 articles, with a focused analysis in this survey on 431 ROS2-specific publications. We categorize these into i.) articles that discuss and analyze core ROS 2 concepts, ii.) articles that propose frameworks or tools for ROS 2, and iii.) articles utilizing ROS 2. Furthermore, we summarize literature findings of ROS 2 challenges, advancements, and future direction in the fields of a.) security, b.) real-time, c.) middleware, d.) embedded and distributed systems, e.) communication reliability and QoS, and f.) multi-robot systems. The methodology involved meticulous data collection and categorization from multiple databases, facilitating an in-depth online accessible resource. Results underscore ROS2’s enhancements in modularity, real-time capabilities, and security, extending its applicability across various robotic platforms and industries. However, challenges in scalability and reliability persist, signaling avenues for future enhancements. This review not only deepens the understanding of ROS2’s contributions but also charts a path for ongoing improvements in robotic systems design. The original data presented in the study are openly available in https://www.ros.riotu-lab.org/.

The urban population in developing countries, especially sub-Saharan Africa, is rapidly increas-ing. As towns and cities grow, so does the demand for fish protein. While flow-through aqua-culture can provide fresh, healthy and nutritious fish protein, it is plagued by extensive land re-quirement as well as effluent discharge, thus, unsuitable for city regions. Alternatively, small-scale Recirculating Aquaculture System (RAS) could improve food and nutritional security (FNS), livelihoods as well as reduce environmental degradation in urban areas despite land and water constraints. The question however remains - what are the key technical, business and managerial issues, surrounding small-scale RAS in urban farming? This study reviews the RAS prototype of the Sustainable Aquaponics for Nutritional and Food Security in Urban Sub-Saharan Africa (SANFU) II project based on mass balance and stock density, relevant for fish survival and/or availability as well as net cash-flow analyses. The results suggest that small-scale RAS are technically and financially viable only with family labor having proper aquaculture monitoring and management skills. Furthermore, access to adequate equipment and inputs as well as electricity for the recirculating system are crucial. Urban innovation actors will adopt RAS if operations are profitable given that family labor is employed.

The rapid growth in the number of interconnected devices on the Internet (referred to as the Internet of Things – IoT), along with the huge volume of data that are exchanged and processed, has created a new landscape in network design and operation. Due to the limited battery size and computational capabilities of IoT nodes, data processing usually takes place on external devices. Since latency minimization is a key concept in modern era networks, edge servers that are in close proximity to IoT nodes gather and process related data, while in some cases data offloading in the cloud might have to take place. The interconnection of a vast number of heterogeneous IoT devices with the edge servers and the cloud, where IoT, edge and cloud converge to form a computing continuum, is also known as IoT-edge-cloud (IEC) continuum. Several key challenges are associated with this new computing systems architectural approach, including: i) design of connection and programming protocols aimed at properly manipulating a huge number of heterogeneous devices over diverse infrastructures; ii) design of efficient task offloading algorithms aimed at optimizing services execution; iii) support for security and privacy enhancements during data transfer to deal with the existent and even unforeseen attacks and threats landscape; iv) scalability, flexibility and reliability guarantees to face the expected mobility for IoT systems, and; v) design of optimal resource allocation mechanisms to make the most out of the available resources. These challenges become even more significant towards the new era of sixth generation (6G) networks, which will be based on the integration of various cutting edge heterogeneous technologies. Therefore, the goal of this survey paper is to present all recent developments in the field of IEC continuum systems, with respect to the aforementioned deployment challenges. In the same context, potential limitations and future challenges are highlighted as well. Finally, indicative use cases are also presented, from an IEC continuum perspective.

Data privacy is a critical concern in health informatics, where the management of sensitive patient information requires robust and secure database systems. This study aims to explore the data privacy challenges faced in health informatics and conduct a comparative analysis of popular Database Management Systems (DBMS), including MySQL, MongoDB, PostgreSQL, and Oracle, with a focus on their effectiveness in safeguarding patient data. Through a review of existing literature, case studies, and interviews with database administrators, the research identifies common privacy threats such as data breaches and unauthorized access. It examines the security features of each DBMS, such as encryption methods, access controls, and user authentication mechanisms. The findings reveal variations in the privacy protection capabilities of different DBMS, highlighting the trade-offs between security, performance, and usability. The study provides recommendations for healthcare organizations in selecting a DBMS that balances privacy with operational needs and offers insights for policymakers to improve regulatory frameworks in health data security. This research contributes to the ongoing discourse on enhancing data privacy in health informatics and suggests directions for future studies to integrate emerging technologies for more secure health data management.

Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is due in large part to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. The existence of these distinctive and unintentional features is attributed to slight manufacturing and assembly variations that exist within and between the components, sub-systems, and systems that comprise an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors that primarily focus on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism, thus we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

This research paper describes the social engineering concepts, techniques, and security countermeasures. This research aims to study various social engineering techniques to find the best countermeasures that would help to reduce social engineering attacks.

In the past ten years, the generative artificial intelligence trend has evolved from simply enhancing the creation of audiovisuals using generative adversarial networks to becoming an entirely disruptive force, transforming operations in various industries and markets, including education and business. The introduction and subsequent widespread popularity of Open AI’s ChatGPT in 2022 raised concerns about the use of Generative AI in professional spaces and its impact on authenticity, innovation, data privacy, and integrity. This study seeks to assess the current applications of this disruptive technology in corporate America and build on that knowledge to provide insight into the current innovative practices and the factors driving its use. The research will further outline some risks associated with generative AI technology in business functions and industries and how this technology is being misused in industries worldwide. Following these revelations, recommendations will be made on the need for regulations surrounding the use of generative AI and the future of this technology for researchers and corporations alike. This study aims to equip corporate strategists and leaders with the knowledge needed to embrace AI, be aware of its possible challenges, and find the best ways to use Generative AI to gain competitive advantage. In addition, it seeks to further equip technology experts and professionals with key information about Generative AI to help them make informed choices when working. The study also aims at research management experts and personnel as it contains vital information that will set the foundation for future research.

In response to the increasing complexity and sophistication of cyber threats, particularly those enhanced by advancements in artificial intelligence, traditional security methods are proving insufficient. This paper explores the zero-trust cybersecurity framework, which operates on the principle of "never trust, always verify" to mitigate vulnerabilities within organizations. Specifically, it examines the applicability of zero-trust principles in environments where large volumes of information are exchanged, such as schools and libraries, highlighting the importance of continuous authentication, least privilege access, and breach assumption. The findings highlight avenues for future research that may help preserve the security of vulnerable organizations.

In recent years, significant progress has been observed in cloud computing. As the number of companies using the cloud increases, so does the need to protect user data. Today, the major challenges facing cloud computing include the security, protection, and processing of data belonging to users. This study aims to investigate security issues and identify appropriate security techniques in the world of cloud computing. Also, the identification of security challenges that may arise in the field of cloud computing in the future has been evaluated. In this research, we used two different research methods: systematic literature review (SLR), and survey and interview with different security experts who work in the field of cloud computing. All the security challenges, most compromised attributes, and identified mitigation techniques from both SLR and survey were presented and discussed.

Secure development is a proactive approach to cyber security. Rather than building a technological solution and then securing it in retrospect, secure development strives to embed good security practices throughout the development process and thereby reduces risk. Unfortunately, evidence suggests secure development is complex, costly, and limited in practice. This article therefore introduces security-focused prototyping as a natural precursor to secure development that embeds security at the beginning of the development process, can be used to discover domain specific security requirements, and can help organisations navigate the complexity of secure development such that the resources and commitment it requires are better understood. Two case studies–one considering the creation of a bespoke web platform and the other considering the application layer of an Internet of Things system–verify the potential of the approach and its ability to discover domain specific security requirements in particular. Future work could build on this work by conducting case studies to further verify the potential of security-focused prototyping and even investigate its capacity to be used as a tool capable of reducing a broader, socio-technical, kind of risk.

Intrusion Detection Systems (IDS) plays a part in modern cyber security, as a result of the increasing need for cyber security systems in the “real” world due to the increasing number of cyber attacks, more sophisticated systems are required in order to prevent these attacks - an IDS can provide this protection. Due to the sophistication of these systems, they must be properly understood, developed and analyzed - research papers can be used as a tool to improve IDS systems. This paper is composed of two main sections: a survey and a taxonomy, providing information, reviews and interpretations from relevant papers, a timeline of important papers, a discussion on the future of IDS and a classification on IDS and how to apply this.

The COVID-19 pandemic is adversely impacting food and nutrition security and requires urgent attention from policymakers. Sustainable intensification of agriculture is one strategy that attempts to increase food production without adversely impacting the environment, by shifting from water-intensive crops to other climate-resistant and nutritious crops. This paper focuses on the Indian state of Andhra Pradesh by studying the impact of shifting 20% of the area under paddy and cotton cultivation to other crops like millets and pulses. Using FAO’s CROPWAT model, along with monsoon forecasts and detailed agricultural data, we simulate the crop water requirements across the study area. We simulate a business-as-usual base case and compare it to multiple crop diversification strategies using various parameters – food, calories, protein production, as well as groundwater and energy consumption. Results from this study indicate that reduced paddy cultivation decreases groundwater and energy consumption by around 9-10%., and a calorie deficit between 4-8% - making up this calorie deficit requires a 20-30% improvement in the yields of millets and pulses. We also propose policy interventions to incentivize the cultivation of nutritious and climate-resistant crops as a sustainable strategy towards strengthening food and nutrition security while lowering the environmental footprint of food production.

Large language models (LLMs) are essential to the field of natural language processing, and as their applications expand, security risks have become increasingly prominent. This paper introduces a novel benchmark for evaluating LLMs security, termed CS-Eval, designed to effectively assess the models' ability to address vulnerabilities. CS-Eval targets seven key security risks: ethical dilemmas, marginal topics, error detection, detailed event handling, cognitive bias, logical reasoning, and privacy identification, and establishes a Multi-Security Hazard Dataset (MSHD). The evaluated models include GPT-4o, Llama-3-70B, Claude-3-Opus, ERNIE-4.0, Abab-6.5, Qwen1.5-110B, Gemini-1.5-Pro, Doubao-Pro, SenseChat-V5, and GLM-4. We analyzed each model's performance in relation to these security risks and provided recommendations for improvement. Experimental results demonstrate varying levels of effectiveness across models, with GPT-4o exhibiting the best overall performance. Moreover, the relationship between security enhancement and model capa-bility is nonlinear, indicating that improving safety requires a multifaceted approach, considering various factors in both development and application.

The sustainability of social security is increasingly important to countries worldwide, yet the impact of regulation on social security remains controversial. Most scholars and policymakers focus primarily on the influence of social regulation on social security, while to varying degrees neglecting the effects of market regulation. This study employs data on the relaxation of market regulation (RMR) from the Marketization Index of China’s Report (MICR) and balanced panel data for 31 provinces in China from 2008 to 2019. Using instrumental variable methods to address endogeneity issues in the model, we empirically evaluate the impact of China's RMR on the level of social security (SSL) and its underlying mechanisms. The conclusion indicates that for every standard deviation increase in the intensity of RMR by local governments, there is a 2.25% increase in SSL. The study also found that social security expenditure and efficiency serve as mediating variables for the impact of RMR on SSL, but it did not support the mediating role of social security fairness. These findings offer valuable insights for optimizing regulation and social security reform in China, as well as useful references for other emerging market economies in identifying priorities for their coordinated regulatory and social security reform.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

Abstract: Service polycentricism, in the face of the pandemic, took on a new significance because of sanitary requirements. Population concentrations in large service centres face a high risk of infection both at their destination and during the trip. Higher-tier services were largely closed during the threat’s peak. In this paper, the focus of the study has been placed on the urban scale of a large city’s polycentricism as seen through the prism of service concentrations, which, to a degree, are commuter destinations, but primarily act as attractors that are not associated with work—they satisfy higher-tier service needs. To formulate the investigative apparatus used to study the functio-spatial structure of Cracow. The current threat, as well as the high probability of similar situations happening in the future, will probably lead to changes in behaviour patterns of consumers and service providers in reference to siting. Local service centres that are accessible to pedestrians, due to safety-related considerations, can gain significant popularity.

On 25 May 2018, the General Data Protection Regulation (GDPR)Article 17, the Right to Erasure (‘Right to be Forgotten’) came into force making it vital for organisations to identify, locate and delete all Personally Identifiable Information (PII) where a valid request is received from a data subject to erase their PII and the contractual period has expired. This must be done without undue delay and the organisation must be able to demonstrate reasonable measures were taken. Failure to comply may incur significant fines, not to mention impact to reputation. Many organisations do not understand their data, and the complexity of a hybrid cloud infrastructure means they do not have the resources to undertake this task. The variety of available tools are quite often unsuitable as they involve restructuring so there is one centralised data repository. This research aims to demonstrate compliance with GDPR’s Article 17 Right to Erasure (‘Right to be Forgotten’) is achievable in a Hybrid cloud environment by following a list of recommendations. However, 100% retrieval, 100% of time will not be possible, but we show that small organisations running an ad-hoc Hybrid cloud environment can demonstrate that reasonable measures were taken to be Right to Erasure (‘Right to be Forgotten’) compliant.

Radio Frequency Identification (RFID) devices use radio waves to relay identifying information to an electronic reader using low-cost RFID Tag. RFID is expected to replace the conventional bar-code identification system due to its advantage like real-time recognition of a considerable number of objects. However, in RFID systems an attacker can get the tag that may lead to various security threats, and the limited computation power of RFID tags can cause delay. Lightweight authentication protocols proposed using cryptographic algorithms (one-way hash function, symmetric key encryption/decryption, and exclusive-OR) in order to cope with these problems. One such lightweight cryptographic protocol has been presented by Gope and Hwang using RFID systems. However, it analyzed in this article that their protocol is infeasible and vulnerable to Collision Attack, Denial-of-service (DoS), and Stolen verifier Attacks. A realistic, lightweight authentication protocol has been presented in this article to ensure protection against the mentioned attacks for IoT based RFID system. The proposed protocol has been formally analyzed using BAN logic and ProVerif as well as also analyzed informally using security requirement. The results show that the proposed protocol outperforms the existing protocols not only in security enhancements but also in terms of computation and communication complexity. Furthermore, the proposed protocol has also been analyzed for storage complexity.

By 2024, artificial intelligence (AI) will have had a revolutionary impact on cyber security, putting the sector at a critical turning point. This article explores the significant influence AI has had on cyber security procedures, outlining the progress made as well as the difficulties faced. artificial intelligence (AI) has transformed threat detection and response systems, allowing for proactive security tactics against a constantly changing array of cyber threats. By utilizing neural networks and machine learning algorithms, artificial intelligence (AI) systems can predict possible security breaches, detect unusual trends, and automatically reduce risks in real-time. These capabilities are unparalleled. Nevertheless, despite these impressive advancements, worries about data privacy, adversarial attacks, and ethical ramifications remain significant. Moreover, the never-ending game of cat and mouse between cyber attackers and defenders continues, requiring constant innovation and adaptability in cyber security strategies driven by Artificial Intelligence. In addition to providing insights into its implications for protecting digital assets and bolstering resilience against cyber threats in the changing cyber security scenario of 2024, this paper summarizes the current state of the artificial intelligence revolution in cyber security.

: Cybersecurity has become a critical area in the digital field in recent years. The expansion of networks has revolutionised the way network structures are organised and managed. However, with increased connectivity and the growing complexity of modern networks, the threat of cyber-attacks has become more intense. As technology continues to advance, it brings both opportunities and challenges. One of the major challenges is the need to secure networks and sensitive data from various malicious activities. Traditional networks have evolved to include Software Defined Network (SDN), which offers a more flexible and programmable framework. Researchers should focus on detecting attacks in SDN because SDN networks are becoming more popular and attractive targets for clients due to their programmability and dynamic nature. The centralised controller, known as the backbone of an SDN, becomes a single point of failure and a potential target for attackers if not properly secured. Researchers need to emphasise the detection of attacks in SDN in order to mitigate these risks. By understanding the potential vulnerabilities and attack methods specific to SDN, researchers can develop effective detection approaches and propose countermeasures. This methodology helps protect the network from potential threats and minimises the impact of successful attacks. Therefore, this survey paper provides a review of Intrusion Detection Systems (IDSs) in Software-Defined Networks (SDN) to provide a thorough understanding of SDN security issues.

The Internet of Things (IoT), as a technology, transforms every day, consumer devices into devices capable of collecting and transmitting data. This momentous progress highlights the need for reliable risk management frameworks that address the potential risks associated with IoT devices across all aspects of life. Traditionally, the feasibility of IoT was limited by the high costs of sensors and their connectivity. Although, recent advancements have greatly reduced costs, enabling widespread connectivity of devices to the Internet. Consequently, numerous IoT devices and networks are left vulnerable without a comprehensive framework for managing these risks effectively. This paper introduces a more systematic framework designed to counter security risks and safeguard IoT devices. This framework takes a step-by-step approach for focusing on mitigating risks arising from inadequate security designs for IoT devices. It involves procedures for risk identification, evaluation, and prioritization which is followed by comprehensive risk analysis and control measures, and because risk evaluation is tedious, we suggest using machine learning (ML) to accelerate the risk evaluation process, boost the precision and consistency of risk assessments, and provide valuable insight, which ultimately enables risk analysts to make better informed and timely decisions. Through multiple case studies, we demonstrate the practicality and efficacy of the framework for evaluating IoT device risks and guiding the implementation of appropriate controls to safeguard devices and networks. We also develop security dashboards that provide visual summaries comparing device values, threat assessments, and risk mitigation costs, aiding in clear communication and prioritization of security measures.

Information security policy compliance plays an important role in reducing the incidence of security breaches in organizations. Recently, information security practices in firms have been widely examined. Therefore, inadequate efforts have been made to assess and systematically review the present study status to offer insights into prior study findings. This systematic literature review investigated the impact of ISPC on reducing the incidence of security breaches in organizations. After inclusion and exclusion criteria process conducted, 42 research publications from Google scholar, Sage, IEEE, ACM, ScienceDirect, Springer, Jstor, Wiley, Taylor & Francis databases (2009 – 2024) included, most of the studies utilized quantitative research methods to explore factors influencing compliance with information security policies, such as employee demographics, organizational strategies, and deterrence mechanisms, A few studies adopted qualitative methods to assess the effectiveness of security programs or to propose measures to enhance compliance. The findings suggested that not only information security policy compliance reduce incidences of security breaches in organizations but also other factors such as employee training, awareness, management support, regular audits, and the integration of advanced security technologies are identified as crucial for reducing security breaches. The implications of these findings for organizational practices and future research directions are discussed, providing valuable insights for policymakers, IT professionals, and researchers aiming to strengthen information security frameworks and reduce the incidence of security breaches.

This paper examines the current state of food security in Pakistan, focusing on the factors influencing the nation's ability to ensure a stable food supply. The analysis covers food production, distribution, accessibility, and stability, while also considering socioeconomic and environmental factors. The role of government regulations and international collaborations is explored in addressing food security challenges. Despite the critical importance of agricultural governance for sustainable food production and economic development, Pakistan's agricultural sector remains under prioritized, suffering from a lack of strategic vision and inconsistent government support. This neglect undermines efforts to achieve food security and stability. The study evaluates the alignment of Pakistan's National Food Security Policy (2018) with global, national, and provincial standards for sustainable agricultural management. A framework based on principles, criteria, and indicators (PCI) was developed, utilizing both quantitative and qualitative methodologies. Forty-three indicators, eleven criteria, and one guiding principle across four dimensions were identified. Thirteen governance tools were assessed using a ratio scale (0–5), with results presented in a policy coherence matrix. The coherence index score of 2.14 indicates significant misalignment among regulatory instruments, highlighting gaps in addressing climate change, low-income food access, service management, and institutional capacity. The study reveals that many food policies in Pakistan are outdated and inconsistent, requiring revision to address local challenges and align with emerging international obligations. The findings provide a comprehensive overview of the current food security landscape in Pakistan, offering insights for more informed decision-making to build a resilient and sustainable food system.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

Although, Artificial intelligence (AI) helps experts with crime analysis, research, and understanding, it has a favourable influence on cyber security. It strengthens the tools that businesses use to safeguard their networks, clients, and workers against dangerous online behaviour. However, artificial intelligence is infamous for requiring a lot of resources. It may not, however, always be relevant. Additionally, it can provide hackers a new tool and advance their abilities. Actually, the VPN industry benefits from AI in the same way. The threat posed by machine learning in AI to user data privacy may be lessened by using a VPN on all of your devices. Because they use machine learning algorithms, VPNs are better equipped to shield their users from internet-based threats. Artificial intelligence (AI) has reportedly being investigated as a means of enhancing internet security for a considerable amount of time, according to Smart Data Collective. We anticipated that AI and machine learning will have a substantial impact on the future of cyber security around two years ago.

Simple Network Management Protocol (SNMP) is extensively utilized for monitoring and managing computer networks. This paper synthesizes and reviews recent research on harnessing SNMP data for developing effective network security solutions driven by artificial intelligence and machine learning techniques. The comprehensive device-level visibility and timely data provided by SNMP offers significant potential to enable high-performance intrusion detection systems, security analytics, and real-time monitoring capabilities to protect against evolving cyber threats. However, inherent vulnerabilities in versions like SNMP v1/v2c persist, especially due to default community strings allowing uncontrolled access. A number of techniques like newer SNMPv3 standard have been proposed to improve security through encryption, authentication, and fine-grained access control. Additionally, general best practices around firewalling, traffic monitoring, and compliance audits are recommended for robust SNMP deployment. The literature reviewed demonstrates SNMP’s capabilities in enabling anomaly detection with over 99% accuracy by applying supervised learning methods like random forests and neural networks to SNMP Management Information Base (MIB) data. The network-centric visibility offered by SNMP's extensive statistics on traffic configurations and behaviors provides an invaluable advantage over packetlevel data for developing intelligent detection models. Beyond intrusion detection systems, SNMP data has also shown significant utility for security analytics like attack pattern discovery, bandwidth modeling, and large-scale threat intelligence by aggregating data from distributed network devices. Further research opportunities exist in areas like feature engineering, model optimization, and continued evaluation of evolving attacks patterns. In conclusion, with appropriate secure configurations, SNMP provides the comprehensive and real-time data needed by modern artificial intelligence security applications in a scalable manner due to its device-centric focus, standardized schema, and ubiquitous deployment

The IoT is recognized as one of the most important areas of future technology and is gaining vast attention from a wide range of industries. Although, after 20 years from the first published literature (2002) the technology (as a whole) is not yet mature. In this study we will review the basics of IoT with a general approach, by addressing the problems of a standard architecture, vulnerabilities and use cases of this promising technology. Moreover, we will review some of the communication protocols that have invented especially for IoT technology, security threats and general implementation challenges.

Currently, life cannot be imagined without the use of bank cards for purchases or money transfers; however, their use provides new opportunities for money launders and terrorist organizations. This paper proposes a Blockchain-enabled transaction scanning (BTS) method for the detection of anomalous actions. The BTS method specifies the rules for outlier detection and rapid movements of funds, which restrict anomalous actions in transactions. The specified rules determine the specific patterns of malicious activities in the transactions. Furthermore, the rules of the BTS method scan the transaction history and provide a list of entities that receive money suspiciously. Finally, the Blockchain-enabled process is used to restrict the money laundering. To validate the performance of the proposed BTS, the Spring Boot application is built based on the Java programming language. Based on experimental results, the proposed BTS method automates the process of investigating transactions and restricts the money laundering incident.

Current research on smart cities is primarily focused on the area of applicability of information and communication technologies. However, in the context of a multidisciplinary approach, it is also necessary to pay attention to the resilience and converged security of individual infrastructures. Converged security represents a particular security type based on a selected spectrum of certain convergent security types of, assuming the creation of a complementary whole. Considering the outputs of the analysis of security breaches manifestations, this kind of security makes it possible to detect emerging security breaches earlier (still in the symptom stage), thus providing a more efficient and targeted solution suitable for building smart city infrastructure. In its essence, the article refers the practical application of the theoretical principles of converged security presented in the publication to a functional sample, deployed and tested in practical conditions of selected smart city infrastructure protection and resilience. Considering the nature of the practical application, the convergence of a wider spectrum of smart security alarm systems in the context of resilience assessment is defined. In the beginning, the general principles of security/ safety and the need for their convergence are presented. In this context, the mathematical model called Converged Resilience Assessment method is presented for better understanding. Subsequently, PSIM and SIEM systems are described as a technological concept that can be used for resilience assessment. The most beneficial part is the structural, process and functional description of the Converged Security and Information Management System (CSIM) using the concept of smart security alarm systems converged security.

Generative AI chatbots have emerged as a significant scientific contribution. They can produce text, images, audio, and video, and their applications are vast and varied in every field. However, it is identified that these chatbots can be used by script kiddies and malicious actors to generate phishing emails, security exploits, and executable payloads that can present notable risks and challenges for cybersecurity. This research provides an extensive review of the current status of the three most popular generative AI chatbots which are ChatGPT, Google Gemini, and Microsoft Bing. The article further reviews the different ways in which an attacker can intentionally use a chatbot for malicious activities and intensify cyber attacks. Moreover, the article provides insights on how an attacker uses certain keyword queries to manipulate chatbot behavior and tricks it into generating content that I won’t otherwise. We also explored the role of AI chatbots in enhancing cyber resilience against sophisticated attacks and their applications in detecting and mitigating security incidents. Finally, we proposed strategies that can empower modern chatbots to defend against such threats so that an attacker will not be able to bypass their content filtering mechanisms.

: Digital communication is becoming an integral component of contemporary society and the use of mobile devices is increasing faster than ever. The function of message communication is becoming more popular as for mobile phone users, a short messaging service (SMS) requires an extremely high level of security. Any SMS that enters a person's smartphone must be considered a private affair, with privacy and security safeguarded. This research study found a novel Deoxyribonucleic acid (DNA) Cryptography method that uses a dynamic DNA sequence table to increase security. Cryptography is a popular approach for ensuring the confidentiality of a message. This paper proposed an Efficient Cryptographic Scheme for SMS (ECSS) algorithm to protect texts received from an Android smartphone. ECSS-based encryption has been identified as a novel method of securing information in the form of DNA molecules that employ DNA strands to conceal the information. This Paper provides the DNA cryptography approach for encrypting and decrypting plain texts in this work. The primary goal of ECSS cryptography is to guarantee secrecy when people transfer data across a network. This paper examines DNA Crypto, the distinction between classical cryptography and DNA Data encryption. An Android-based app is created to perform the proposed technique and the results show that the algorithms produce a consistent encryption result in which the length of the message before encryption is always the same as the length of the message after decryption. The suggested security solution has the potential to greatly improve the security of mobile communication. This Paper also provides experimental results for our chat application performance, such as received text message correctness, cipher text, average encryption time and average decryption time. According to the findings/results of this study, the speed of average encryption and decryption of the description is roughly 0.011 and 0.037 sec and prevents plaintext and dictionary attacks.

Security in the Internet of Things (IoT) is a critical concern due to the growing number of connected devices and the limited resources to implement robust security mechanisms in most of them. Adopting standard assessment frameworks, such as CVSS (Common Vulnerability Score System), allows for systematic vulnerability assessment. At the same time, CVE (Common Vulnerabilities and Exposures) records provide unique identifiers for known security issues, making it easier to share information. However, comparing security solutions is still challenging due to the diverse nature of IoT devices and the ever-evolving threat landscape, requiring continual innovation and collaboration between stakeholders. Furthermore, there still needs to be a simple way to compare technologies and solutions, taking into account the security provided by them. In this sense, in this article, we propose a tool for calculating a security score based on the CVSS index of each existing vulnerability in a given technology. We explain how the tool performs the calculation, discuss some challenges, and propose improvements for future work.

Sensors and actuators are fundamental units in Cyber-Physical and Internet of Things systems. Because they are included in a variety of systems, using many technologies, it is very useful to characterize their functions abstractly by describing them as Abstract Entity Patterns (AEPs), that are patterns that describe abstract conceptual entities. For concreteness, we study them here in the context of autonomous cars. An autonomous car is a complex system because, in ad-dition to its own complex design, it interacts with other vehicles and with the surrounding in-frastructure. To handle these functions, it must incorporate various technologies from different sources. An autonomous car is an example of a Cyber-Physical System, where some of its func-tions are performed by Internet of Things units. Sensors are extensively used in autonomous cars to measure physical quantities; actuators are commanded by controllers to perform appro-priate physical actions. From AEPs we can derive concrete patterns, a structure combining re-lated AEPs is an Entity Solution Frame (ESF). Both sensors and actuators are susceptible to mali-cious attacks due to the large attack surface of the system where they are used. Our work is in-tended to make autonomous cars more secure, which also increases their safety. Our final objec-tive is to build a Security Solution Frame for sensors and actuators of autonomous cars that will facilitate their secure design. A Security Solution Frame is a solution structure that groups to-gether and organizes related security patterns. This article is the first stage of a secure unit that can be used to design not only secure autonomous cars but also any system where sensors and actuators are used. This paper concentrates on AEPs and ESFs for sensors and actuators; that is, on the functional aspects of these devices.

Background: Cyber security is used to save the important data from getting hacked, took by some unknown access. It makes our environment a safe place for us to work out or share our information and since privacy is almost everyone’s top priority, there must be the surety of saving the private information. Methods: In today’s world cyber security is really important as there are now many devices, websites, new technology which makes it a lot easier for hackers to get into anyone’s files which stores crucial data. However, many steps can be reserved to protect the data such as; educating your employees with how to prevent your files or computers from getting hacked, avoid clicking on websites that don’t seem safe, use firewalls, antimalware system and most importantly make sure to keep passwords that are hard to guess and try to go for a face recognition instead of pins and passcodes. Results and Conclusion: This all does not only makes using technology safe but also favors your business for the safety of employees working within the organization. The customers being assured about their data being safe, improves productivity since viruses can slow down computers which may trigger the focus of staff during the working hours. It could save your system from adware; the links of websites are also prevented from slow internet speeds. There is no denying at the fact that cyber security is a major requirement for everyone who is indulged with technology today.

Coronavirus is now a significant human pathogen with the emergence of SARS-CoV-2. Until now there has been no data to support a threat to agricultural industries. Using a comparative genomic protein analysis, this study examined the angiotensin-converting enzyme II (ACEII) gene of 17 animal species with an emphasis on agriculture. To determine viral vulnerability the 20 known SARS-CoV-2 receptor-binding domain (RBD)/ACEII receptor interaction sites were compared to determine their potential susceptibility to the SARS-CoV-2 virus. With the known bat host’s (XP_032963186) number of binding sites as a threshold, we note that ALL animal species examined in this study contained significant numbers (≥10) of SARS-CoV-2 binding sites and could be at risk for SARS-CoV-2 infection. The data from this study suggest SARS-CoV-2 imposes a grave threat to the safety and security of the agricultural industry. Urgent studies are needed to determine if infected animals can transmit SARS-CoV-2 before and/or after processing.

Food is one of the basic necessities for all living things in this globe to grow, maintain life and develop. It is a source of energy for almost all bodily functions and it directly affects our health status and how we feel each day and the future. Yet, there are millions of people around the globe who do not have the sumptuousness to provide enough food to themselves and their beloved ones. Ethiopia is one of the highly food insecure countries in the world, it's name has been illustrious for famine and drought for decades. The study's main aim is to assess the livelihood resources and strategies of the smallholding farmers in their strive to achieve food security at household level. For this particular study, from Tigray region, Kilte Awelalo was taken as a study area and three Tabias were selected; Ayenalem, Genfel and Tahetay Adikesanded. The total sample number of households from these three Tabias was 370 and a formal interview schedule was employed to collect relevant primary inputs for the study. The study found that farmland holding size and its fertility as major impacting factors behind the livelihood strategies and food security condition of the smallholding farmers. It was also found that about one third of the total sample population were chronically food insecure, whereas about half of the population were transitory food insecure and rest very little number of households were food secure.

This research paper presents a detailed analysis of a Lightweight Block Cipher’s power consumption and security features (LWBC), specifically designed for IoT applications. To accurately measure energy consumption during the execution of the LWBC algorithm, we utilised the Qoitech Otii Arc, a specialised tool for optimising energy usage. Our experimental setup involved using the Otii Arc as a power source for an Arduino NodeMCU V3, running the LWBC security algorithm. Our methodology focused on energy consumption analysis using the shunt resistor technique. Our findings reveal that the LWBC is highly efficient and provides an effective solution for energy-limited IoT devices. We also conducted a comparative analysis of the proposed cipher against established LWBCs, which demonstrated its superior performance in terms of energy consumption per bit. The proposed LWBC was evaluated based on various key dimensions such as power efficiency, key and block size, rounds, cipher architecture, gate area, ROM, latency, and throughput. The results of our analysis indicate that the proposed LWBC is a promising cryptographic solution for energy-conscious and resource-limited IoT applications.

Companies operating in the PayTech and online e-commerce sectors play a crucial role in critical infrastructure, functioning within the dynamic digital landscape. This study focuses on the recovery process after cyber-attacks and examines the contrasting perspectives of internal and external professionals. The research reveals notable differences in the perceptions of recovery strategies between internal stakeholders such as investor relations, reputation management, and Chief Information Security Officers, representing companies belonging to critical infrastructure and external auditors, who provide just and emergency support and perform specific tasks. Importantly, the study underscores the current attitudes towards future information security strategies and their influence on the financial recovery and reputation of reliable companies following cyber incidents. This research contributes to the existing knowledge by shedding light on the perspectives of both a company's internal and external specialists involved in the recovery process and cyber resilience strategies in critical infrastructure sectors.

Predictive maintenance (PdM) represents a transformative approach to ensuring the security and reliability of critical infrastructure systems. This proactive maintenance strategy leverages advanced data analytics, machine learning, and sensor technology to anticipate and address potential failures before they occur. By continuously monitoring the health of infrastructure components and analyzing performance data, predictive maintenance enables timely interventions, minimizes unplanned downtimes, and enhances overall system resilience. This paper explores the application of predictive maintenance in safeguarding critical infrastructure, focusing on its impact on security, operational efficiency, and cost-effectiveness. Case studies demonstrate how PdM has been successfully implemented across various sectors, including transportation, energy, and utilities, highlighting its role in mitigating risks and extending the lifespan of essential assets. The findings underscore the importance of integrating predictive maintenance into security strategies to protect vital infrastructure from emerging threats and ensure uninterrupted service delivery.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Supply chain disruptions, a pandemic, and war in Ukraine have exposed faultlines in a globalised food system that depends on a few staple crops grown in a few exporting regions and transported to consumers around the world. In the UK, just three crops, (wheat, barley, and oilseed rape), account for 75 per cent of the UK’s 4.5 million hectares of arable land whilst the country imports around half its food - nearly 40 per cent from just four EU countries (the Netherlands, Ireland, Germany, and France). Poor diets contribute to one in seven deaths in the UK, 63 per cent of the population is overweight or obese and health inequality is increasing between the poorest and most affluent regions. The food security and health of the UK population is therefore dependent on a small number of locally grown crops, vulnerable supply chains, and an unhealthy, obesogenic diet. The UK food system must diversify if it is to become food and nutritionally secure, meet its climate and biodiversity goals and have a healthy and active population. Climate resilient and nutritious underutilised crops can help diversify the UK agrifood system but, research and investment in them is sporadic, piecemeal, and unfocused. In this paper we compare two approaches to identifying potentially suitable underutilised crops for the UK. The first, based on Defra Project CH0224 was delivered through literature and database searches and expertise of growers, advisers, breeders, seed suppliers, processors, traders, and researchers. The second, used the CropBASE digital knowledge base for underutilised crops described in Jahanshiri et. al. (2023). The two approaches produced no single crop that was common to both shortlists. We propose that the analytical and predictive tools derived from CropBASE could be combined with local knowledge and expertise from the Defra project to provide a common framework for the identification of underutilised crops that are best suited to local UK circumstances now and in climates of the future.

Internet of Things (IoT) provides a huge business value for customers, organizations, and governments due to the developments of so many applications in different sectors like energy and healthcare. Nevertheless, as a new emerging technology, IoT faces several security concerns that are more challenging than conventional Internet because of its limited resources as well as its complex ecosystem. Toward this end, we first highlight IoT security challenges and briefly discuss its security goals like confidentiality and integrity. Second, we discuss the most common attacks against IoT, along with their violated security goals. We also review the existing frameworks of security and privacy guidelines for IoT and illustrate their shortcomings. Third, we propose a novel framework for securing IoT objects, the key objective of which is to assign different Security Level Certificates (SLCs) for IoT objects based on their hardware capabilities and protection measures. Objects with SLCs, therefore, will be able to communicate with each other or with the Internet in a secure manner. The proposed framework is composed of five main phases. In phase 1, we classify IoT assets into four components: (i) physical objects, (ii) protocols, (iii) data at rest, and (iv) software, which includes operating systems, middlewares, and applications. We also classify IoT objects into five categories based on their hardware capabilities. In phase 2, we propose security and privacy guidelines for previously mentioned IoT assets, along with their protection measures. In phase 3, we classify protection measures into five SLCs, and then we assign different SLCs for IoT objects. In phase 4: we develop a communication plan between objects based on their SLCs. In phase 5, we propose a four-step method to seamlessly integrate our objects with legacy objects ( objects are not developed according to our framework). Fourth, the feasibility and application of this framework are illustrated using smart homes as a case study. Finally, we investigate how our framework would lessen several attacks and threats against IoT like routing attacks and physical damage. We also provide qualitative arguments to show that this framework could be utilized to solve some of IoT security challenges such as tight resource constrains. Moreover, we discuss the shortcomings of our suggested framework.

This paper explores the ever-changing topic of cybersecurity and provides a summary of the most recent developments. In a time of unparalleled digital revolution and changing threat environments, being aware of new trends is essential to managing risks and protecting important assets. To highlight significant advancements in fields like artificial intelligence and machine learning for threat detection, the proliferation of IoT devices and related security challenges, the emergence of quantum-resistant cryptography, the development of zero trust architectures, and the growing significance of cybersecurity awareness and education, the report synthesizes insights from industry experts, academic research, and real-world case studies. The goal of the report's analysis of these patterns is to provide stakeholders with useful information so they may modify their cybersecurity plans and strengthen defenses against new threats. Cybersecurity plays a significant role in the IT industry.

Reconfigurable computing is becoming ubiquitous in the form of consumer-based Internet of Things (IoT) devices. Reconfigurable computing architectures have found their place in safety-critical infrastructures such as the automotive industry. As the target architecture evolves, it also needs to be updated remotely on the target platform. This process is susceptible to remote hijacking, where the attacker can maliciously update the reconfigurable hardware target with tainted hardware configuration. This paper proposes an architecture of establishing Root of Trust at the hardware level using cryptographic co-processors and Trusted Platform Modules (TPMs) and enable over the air updates. The proposed framework implements secure boot protocol on Xilinx based FPGAs. The project demonstrates the configuration of the bitstream, boot process integration with TPM and secure over-the-air updates for the hardware reconfiguration.

This paper addresses the growing concerns surrounding cloud computing security and highlights key strategies for ensuring the privacy and protection of data in information-critical industries. Cloud computing has revolutionized the field of information technology, providing convenience and efficiency in data processing. However, it has also brought to light significant challenges in terms of data security and privacy. Given the rise in cyber threats and the potential risks associated with storing sensitive information on cloud servers, critical industries such as healthcare and banking have been hesitant to adopt cloud computing as a trusted solution for their data needs. This work examines the specific security concerns that these industries face and proposes measures to mitigate them. Further, the paper explores the barriers that hinder the adoption of cloud computing in critical industries and offers solutions to address these concerns.

This study investigates the use of flours derived from the leaves of Pereskia aculeata and Pereskia grandifolia, commonly known as ora-pro-nóbis, in pasta products. The motivation stems from the search for nutritious and affordable dietary alternatives that promote food security. The objective is to produce flours from the leaves of these species through convection drying and to assess their physicochemical quality and potential application in spaghetti-type pasta. The leaves were dehydrated and processed into flour, which was then incorporated into pasta formulations in varying concentrations from 1% to 7%. The resulting samples underwent physicochemical, antioxidant, toxicity, and mineral composition analyses. Significant differences were observed between the flours of P. aculeata and P. grandifolia in terms of protein, lipid, carbohydrate, and bioactive compound content. P. grandifolia was noted for its higher protein and mineral content, while P. aculeata exhibited superior antioxidant activity. The pasta enriched with these flours demonstrated improvements in nutrient content and changes in colorimetric properties. The study concludes that ora-pro-nóbis flours possess high nutritional value and are viable for application in pasta products, contributing to a healthier and more diversified diet without compromising the sensory characteristics of the products.

The management of large medical files is a critical challenge in the health sector. Conventional systems have security, scalability, and efficiency deficiencies. This project proposes a hybrid architecture between blockchain and cloud computing to address these challenges. Blockchain ensures the immutability and traceability of medical records, while the cloud allows scalable and efficient storage. Together, these technologies can transform data management in electronic health record applications. The essential objective of this research is to thoroughly look at the existing strategies, devices, and conventions that encourage a secure interaction between blockchain applications and the cloud. Special emphasis is set on maintaining the integrity and security of the blockchain while tackling the potential and efficiency of cloud infrastructures. Through a meticulous investigation of these approaches, along with their successful usage and encountered challenges, this paper offers a comprehensive overview of the present state of this integration. Altogether, this paper does not propose new concepts or developments. Its central focus is to provide a comprehensive and insightful examination of the modern landscape concerning the integration of blockchain and cloud advances. By shedding light on the existing approaches and assessing their viability, this work aims to highlight the current challenges and build up a solid establishment for future development. This, in turn, will cultivate a consistent integration of blockchain security with the dynamic potential of cloud computing while guaranteeing information integrity and security remain uncompromised. In conclusion, this paper serves as a important resource for analysts, specialists, and partners looking for to dig into and development the integration of blockchain and cloud innovations, subsequently contributing to a more secure and productive cloud environment.

in our increasingly interconnected world, sensor networks are critical in gathering and sending data for various applications, from environmental monitoring and industrial automation to healthcare and smart cities. However, as sensor networks expand in importance, so does the need to solve the multidimensional concerns of security, privacy, and forensics. This article explores the complex world of sensor network security, the delicate balance between data privacy and utility, and the emerging area of sensor network forensics. This article focuses on risk assessment of a network.

Wireless sensor networks are usually applied in hostile areas where nodes are easy to be moni-tored and captured by adversary. Key distribution is an essential primitive to provide most of security mechanism. However, the characteristic of limited resources of sensors restricts the direct use of conventional key distribution schemes. In this paper, a complete security key distribution scheme based on asymmetric cryptography technology is proposed in both static and mobile scenarios. Mutual authentication is guaranteed using challenge-response mechanism. The per-formance evaluation and security analysis show that the proposed scheme with low complexity not only provides better security for wireless sensor networks, but also reduces storage overhead and key exposure risks.

The recent COVID-19 pandemic has showcased the implications of using virtual private networks (VPNs) as home-working is now common. Establishing the current state of knowledge on VPNs and their processes is vital. This corroborates an up to date understanding on the fundamentals of VPNs and their usage in today’s society; it informs on how to better use VPNs too. Insight into the security issues VPNs face and possible solutions to these allows for the identification of gaps for potential future research. Addressing these gaps would then indicate how to further improve VPNs, making sure VPNs are indeed beneficial to users.

Ethiopia has 74.3 million hectares of arable land and an irrigation potential of 3,088,395 hectares. Despite the country's vast irrigation potential, it hasn’t benefited much. A huge percentage of Ethiopia’s population is currently food insecure and a very significant portion lives under the national poverty line. Studies have confirmed the role of irrigation in alleviating poverty and promoting food security. Irrigation plays a huge role in poverty reduction, especially in developing countries like Ethiopia where agriculture is the backbone of the economy and the major source of employment. Irrigation does so by boosting farmer incomes, improving crop productivity, providing more employment and better pay rate, and contributing to the national GDP in the long run. Irrigation also promotes food security by contributing to all the dimensions including, food availability, access to food, food utilization, and stability. Given such importance of irrigation in poverty and hunger alleviation, it’s believed that the development of the sector massively contributes to the realization of sustainable development goals (SDG). Hence, this article voices the need for exploiting the huge irrigation potential and improving the irrigation sector, in order for the country to pull its population out of the misery of hunger and poverty.

Nowadays, people live amidst the smart home domain, business opportunities in the industrial smart city and health care, though, along with concerns about security. Security is central for IoT systems to protect sensitive data and infrastructure, whilst security issues become increasingly expensive, in particular in Industrial Internet of Things (IIoT) domains. Nonetheless, there are some key challenges for dealing with those security issues in IoT domains: Applications operate in distributed environments such as Blockchain, varied smart objects are used, and sensors are limited in what comes to machine resources. In this way, traditional security does not fit in IoT systems. In this vein, the issue of cyber security has become paramount to the Internet of Things (IoT) and Industrial Internet of Things (IIoT) in mitigating cyber security risk for organizations and end users. New cyber security technologies / applications present improvements for IoT security management. Nevertheless, there is a gap on the effectiveness of IoT cyber risk solutions. This review article discusses the, trends around opportunities and threats in cyber security for IIoT.

Data could be in many forms like text, image, audio, video and many others. Amongst these data, processing image is of important concern even with sophisticated technologies. Generally, images consume more storage space and processing time. This work aims in attaining a novel image encryption strategy that consumes very less computational time with maximum security. The idea is to split the image into blocks and rearrange the pixels in diagonal fashion to achieve confusion. Next, the blocks are combined to form sub images and one block of the sub image is subjected to Secure Hash Algorithm (SHA 512 bits). The SHA bits are XORed with the remaining blocks of the sub image. Finally, the entire image is encrypted using Advanced Encryption Standard (AES). Decryption process is the exact reverse of encryption process. The computational overhead is very low, and security is efficient. The results are compared with existing encryption techniques.

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

Cloud robot is becoming popular and security of cloud robot is important. However, the researches of cloud robot safety are a few. This work develops a security policy to defense DDoS attack of cloud robot. In this policy, complex, but accurate calculation models are deployed on the cloud, simple but efficient calculation models are deployed on the robot. In the cloud, there are master server and standby server. The master server transfers parameters of complex but accurate models to the standby server periodically and the master executes the start-stop backup policy. Specifically, this work proposes and proves an algorithm to dynamically adjust the interval of parameter transfer. According to a PDRA feature of Netflow, when DDoS attacks, the master server sends warning signals to the robot and standby server. The robot runs local models to avoid stopping work until it is connected to the standby server. Then, standby server provides service to the robot until the master server recover. Finally, this work implemented a gesture recognition cloud robot based on convolutional neural network, hidden Markov model and PDRA feature of Netflow to verify the policy. Experiment shows that the security policy to defense DDoS attack for cloud robot is effective.

The emergence of the Internet of Things (loT) has led to remarkable advancements in various sectors such as urbanization, manufacturing, and healthcare, intending to enhance the quality of life and contribute to the world economy. However, by the conclusion of 2024, ti is projected that the global network of the Internet of Things (IoT) will encompass over 207billion interconnected devices. The immense data collected and exchanged are often stored in centralized servers, leading to vulnerabilities such as data breaches. Blockchain technology, originally developed for cryptocurrency transactions, provides a possible solution for these challenges with its decentralized, immutable, and encrypted features. This survey provides a comprehensive discussion regarding the fusion of Blockchain technology and the loT, covering architectural alignment, application, security, and limitations. The contributions of this survey include an overview of Blockchain technology and its secure applications, the integration of Blockchain into IoT , an examination of suitable methods for Blockchain integration,and a taxonomy or security and limitations of loT and blockchain integration.

The aim of the study is to characterize the U.S. and Lithuanian defense cooperation from 1990–2020 including arms procurements. Findings suggest close defense cooperation between countries at Presidential, Congressional, and Defense Ministry/Secretary levels. The most developed cooperation areas are joint participation in international operations, improvements of the Lithuanian Armed Forces’ capabilities, and an increase of the U.S. military presence in the Baltic region. An analysis of Lithuanian defense procurements between 1990–2020 indicates that the majority of defense armaments were acquired from the U.S. using the Foreign Military Sales venue while other options to obtain armaments were not used.

Artificial Intelligence has transformed the cyber security industry by enabling organizations to systematize and enlarge outdated safety procedures. AI can provide more effective threat detection and response capabilities, enhance vulnerability management, and improve compliance and governance. AI technologies such as machine learning, natural language processing, behavioral analytics, and deep learning can enhance cyber security defenses and protect against a wide range of cyber threats, including malware, phishing attacks, and insider threats.Theoretical underpinnings of AI in cyber security, such as machine learning, natural language processing, behavioral analytics, and deep learning, are discussed. The advantages of using AI in cyber security are discussed including speed and accuracy, continuous learning and adaptation, and efficiency and scalability. It's important to note that AI is not a silver bullet for cyber security and should be used in conjunction with other security measures to provide a comprehensive defense strategy.AI has transformed the way cyber security operates in today's digital age. By analyzing vast amounts of data quickly and accurately it has become a valuable tool for organizations looking to protect their assets from cyber threats.

Mobile devices have become tools we spend our free time where we carry them with us every moment, they allow us to interact with the environment, we immortalize the moment when necessary. These devices which we spend most of our daily life become very common in recent years and even there are unique business areas emerged. It was announced that the number of people using smartphones is over than 2.5 billion in the first quarter of 2016. As people become more addicted to mobile technology, they become the target of malevolent people. A huge increase in the number of mobile malware is observed as the number of the users increase. Billions of users at risk day by day due to the development of the methods. We have addressed the recent methods used and the types of malware that target mobile devices in our study. We have mentioned the proxy server and reverse proxy server operation logic. We discuss the method of turning mobile devices into reverse proxy servers, risks involved and protection methods.

The proliferation of cloud computing has given rise to challenges in security that established perimeter-based models cannot easily adapt to. The new buzzword Zero Trust, which is based on the principle of "never trust, always verify," has been looked up as one of the promising solutions. Is Zero Trust a certainty or pie in the sky for security in the cloud? This is discussed further in this paper. This philosophical approach leads us through the earlier work and scientific literature available on the subject, presenting Zero Trust's capabilities, advantages when introduced into cloud security architecture, and obstacles about its incorporation. The study finds that Zero Trust holds the potential to enhance cloud security significantly, but it is no silver bullet and demands careful implementation and further studies to understand its intricacies as well as pitfalls.

Surgical knots are sequences of half-knots (H) or half-hitches (S), defined by their number of throws, by an opposite or similar rotation compared with the previous one and for half-hitches, whether sliding (s) or blocking (b). Opposite rotation results in (more secure) symmetric (s) knots, similar rotation in asymmetric (a) knots, and changing active and passive ends has the same effect as changing rotation. Loop security is the force to keep tissue together after a first half-knot or sliding half-hitches. With polyfilament sutures H2, H3, SSs and SSsSsSs have a loop security of 10, 18, 28 and 48 Newton (N), respectively. With monofilament sutures, they are only 7, 16, 18 and 25N. Since many knots can reorganise, the definition of knot security as the force at which the knot opens or the suture breaks should be replaced by the clinically more relevant percentage of clinically dangerous and insecure knots. Secure knots with polyfilament sutures require a minimum of 4 or 5 throws, but the risk of destabilisation is high. With monofilament sutures, only 2 symmetric+4 asymmetric blocking half-hitches are secure. In conclusion, half-hitch sequences are recommended since mandatory for monofilament sutures, adding flexibility for loop security with less risk of destabilisation.

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario. However currently users rely on classical computer security schemes, which have known shortcomings. In this chapter we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking.

Internet of Things (IoT) opens new horizons by enabling automated procedures without human interaction using IP connectivity. IoT deals with devices, called things which are represented as any item from our daily life that is enhanced with computing or communication facilities. Among various mobile communications, Zigbee communication is broadly used in controlling or monitoring applications due to its low data rate and low power consumption. Securing IoT systems have been the main concern for the research community. In this paper, different security-threats of Zigbee networks in IoT platform have been addressed to predict the potential security threats of Zigbee protocol and a Security Improvement Framework (SIF) has been designed for intelligent monitoring in an office environment. Our proposed SIF can predict and protect various potential malicious attacks in the Zigbee network and respond accordingly through a notification to the system administrator. This framework (SIF) is designed to make automated decisions immediately based on real-time data which are defined by the system administrator. Finally, the designed SIF has been implemented in an office security system as a case study for real-time monitoring. This office security system is evaluated based on the capacity of detecting potential security attacks. The evaluation results show that the proposed SIF is capable of detecting and protecting several potential security attacks efficiently enabling more secure way of intelligent monitoring.

Food insecurity remains a significant developmental challenge, particularly in rural areas, despite ongoing efforts to mitigate it. To better understand the persistence of this issue, this study investigates the causes of household food insecurity and the coping strategies employed by households in Zorkor Goo, Zorkor Kanga, and Zorkor Kadaare communities within the Bongo District of Ghana’s Upper East Region. A descriptive cross-sectional survey was conducted, sampling 196 households systematically from these communities. Data were collected through a structured questionnaire covering socio-demographic factors, causes of food insecurity, household coping strategies, and dietary intake. The analysis was performed using SPSS (Version 21.0), with food security status and dietary intake assessed via the FANTA Household Food Insecurity Access Scale (HFIAS) and Food Consumption Score, respectively. The study identified key factors contributing to food insecurity, including soil infertility, poor rainfall patterns, food spoilage, extravagant funerals, large family size, high unemployment, and lack of education. Major coping strategies among households included meal skipping, reducing meal size and frequency, migration, asset sales, and basket weaving. The findings revealed that 96.4% of households were severely food insecure, and over two-thirds (67.3%) exhibited poor dietary consumption. Notably, household employment levels, education, and food consumption scores were significantly associated with food insecurity (P<0.001). These findings underscore the need for targeted interventions addressing the root causes of food insecurity and enhancing household resilience in these communities.

The aimed of this study was to explore the current state of knowledge of agrobiodiversity to improve nutrition and health. The study focused on wild mushrooms commonly consumed in North-Eastern of Thailand in term of ecology, cooking and preservation method and folk remedies. The monosaccharide, sugar alcohol, glucans and carbohydrate polymer content and the glycemic index of these wild mushrooms was determined using Enzymatic method. The mushrooms collected were belong to three biological groups and mostly saprotrophic and symbiotic. The most abundant mushrooms are Amanita, Boletus and Russula and followed by Calostoma sp., Astraeus asiaticus C. Phosri and Astraeus odoratus C. Phosri. Wild edible mushrooms can be used include food and medicinal purpose. Cooking methods utilized in the area are steaming, boiling and grilling. The carbohydrate profiles results showed that glucose is the major monosaccharide detected in mushroom samples. Xylitol and inositol was found in all mushroom sample while mannitol and arabitol was detected in some samples. Glucan was presented in all mushroom samples ranged from 8.03-31.1 mg/g DW. All mushrooms are classified as low GI food. Therefore, this finding provide important information that may be adopted to enhance and promote the utilization of wild mushroom to improve nutrition security and sustainability.

This research study aims to explore how during COVID-19, the adoption of online banking is impacted by various factors such as perceived usefulness, perceived ease of use, perceived security, and trust. The data were collected using the primary questionnaire with 98 respondents. The study investigates the direct effect using the PLS-SEM method, and the indirect effects are analyzed using mediation analysis. The study indicates that perceived security is an important factor that impacts the adoption of online banking by self-help groups in India. Trust in online banking without allaying the fears of banking online does not lead to the adoption of technology. Perceived ease of use and ease of usage directly impact the adoption of online banking by the members of self-help groups in India. The study is the first-ever study to measure the indirect impact of trust on the intention to use online banking by the members of self-help groups in India This study has far-reaching implications for policymakers and banks. To increase the adoption of online banking, it is important to allay the fears of security among the members of self-help groups in India.

A modern binary executable is a composition of various networks. Control flow graphs are commonly used to represent an executable program in labeled datasets used for classification tasks. Control flow and term representations are widely adopted, but provide only a partial view of program semantics. This study is an empirical analysis of the networks composing malicious binaries in order to provide a complete representation of the structural properties of a program. This is accomplished by the measurement of structural properties of program networks in a malicious binary executable dataset. We demonstrate the presence of Scale-Free properties of network structure for program data dependency and control flow graphs, and show that data dependency graphs also have Small-World structural properties. We show that program data dependency graphs have a degree correlation that is structurally disassortative, and that control flow graphs have a neutral degree assortativity, indicating the use of random graphs to model the structural properties of program control flow graphs would show increased accuracy. By providing an increase in feature resolution within labeled datasets of executable programs we provide a quantitative basis to interpret the results of classifiers trained on CFG graph features. An increase in feature resolution allows for the structural properties of program classes to be analyzed for patterns as well as their component parts. By capturing a complete picture of program graphs we can enable theoretical solutions for the mapping a program's operational semantics to its structure.

Disasters such as the 2015-2018 drought in South Africa usually negatively impact agricultural water, especially in smallholder farming systems. This study assessed the availability of irrigation water, performance of irrigation infrastructure, and water governance systems in Genadendal, Western Cape, with a focus on smallholder farmers. Data for streamflow, dam levels, and rainfall were acquired from water institutions and analysed using Microsoft Excel. The performance of the infrastructure and water governance were assessed based on the perceptions of smallholder farmers and key informants. A questionnaire was administered to eight smallholder farmers, followed by a focus group discussion with 15 smallholder farmers. Interviews were conducted with eight key informants who worked in water-related institutions. Qualitative data from the interviews were analysed using thematic content analysis. The study showed adequate agricultural water resources during the 2015-2018 drought period for the smallholder farmers who relied on water from dams. However, smallholder farmers who relied on the Riviersonderend River experienced severe water shortages during the same period. The findings also showed that there was poor performance of irrigation water infrastructure due to inadequate maintenance. Lack of coordination among institutions, insufficient funding, political interference, fragmentation of roles and responsibilities, lack of human resources, and farmers’ lack of participation and commitment in water resources management contributed to the dysfunctionality of irrigation water governance systems, leading to water insecurity. This study recommends adequate funding for the operation and maintenance of infrastructure. Furthermore, water institutions need to support smallholder farmers with training skills in agricultural water management and infrastructure maintenance. Further studies are recommended to quantify the water lost due to the poor performance of water infrastructure and to develop effective water governance in Genadendal.

Cyber-threat is now a global concern to all internet users, as the rate of crimes has tremendously changed from physical to virtual. Implementing awareness programs at various organization levels is required, Also there is a need to know the methodologies used in measuring cybersecurity awareness program effectiveness. The paper aims to review and explore the previous methodologies applied, target organizations, and application of Machine Learning techniques in the assessment of awareness programs. Systematic literature review techniques were used to conduct the search using pre-defined keywords and published papers from 2006 to 2019, the information was used in providing an answer to the research questions. The finding shows questionnaire was widely used as a method of evaluating cybersecurity awareness and also organizations implement more awareness programs, the gap found is the lack of using ML in assessing the effectiveness of design, and implemented cybersecurity awareness programs

The general objective of the study is to analyze food insecurity determinants mainly related to food price at macro level. The study used a time series data from the year 1988 quarter one to 2018 quarter four. Our empirical finding on the macroeconomic model show the short term consequence of food price change is positive with food security (negative with food insecurity). The long term consequence of food price change is, however, negative with food security (positive with food insecurity) at macro level. From the variance decomposition, it can be learned that the speed by which the undernourished people decrease in number in the short term because of the inflation happened is higher than the speed by which the number of undernourished people increase in the long run. At macro level, concern should be made to the supply side for food security improvement.

Decentralized Finance (DeFi) is an emerging and revolutionizing field with notable uncertainties of reliability to be used on a mass scale. On the other hand, Artificial Intelligence (AI) has proved to be a crucial helping tool in numerous domains. In this study, we present a systematic review of the utility of AI in Defi in terms of impact, reliability, and security and conduct exhaustive analysis. We further conclude from our extensive literature review that we can identify possible new research opportunities in AI to bridge the gap of trust between peers and make the integration of DeFi more agile in the near future.

Short Message Service (SMS) is becoming the secure medium of communication due to large-scale global coverage, reliability, and power efficiency. As person--to--person (P2P) messaging is less secure than application-to-person (A2P) messaging, anyone can send a message, leading to the attack. Attackers mistreat this opportunity to spread malicious content, perform harmful activities, and abuse other people, commonly known as spam. Moreover, such messages can waste a lot of time, and important messages are sometimes overlooked. As a result, accurate spam detection in SMS and its computational time are burning issues. In this paper, we conduct six different experiments to detect SMS spam from the dataset of 5574 messages using machine learning classifiers such as Multinomial Naïve Bayes (MNB) and Support Vector Machine (SVM), considering variations of \textit{Term Frequency-- Inverse Document Frequency (TF--IDF)} features for exploring the trade-off among accuracy, F1-score and computational time. The experiments achieve the best result of the accuracy of 98.50\%, F1--score of 98\%, and area under roc curve (AUC) of 0.97 for multinomial naïve bayes classifier with TF--IDF after stemming.

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

The study of values at European and global level has gained momentum along with the theory of values launched by Schwartz. Values are dormant, hidden, unobservable and impossible to measure directly. They exist somewhere within the human being, manifested by different attitudes, depending on their degree of generality. The paradigm of basic human values implies shared universal values that are encountered in cultures all over the world. The theory of values designed by Schwartz has refined the pool of 19 basic individual values representing principles in individuals and group, further describing the dynamic relationships among them. This study presents practical evidence of the dynamics between individual values, the dynamics theorized by Schwartz in 2012. Following the development of an online questionnaire made up of 46 items with online responses options on a Lickert scale from 1 to 6, where 1 represents less important and 6 represent very important, 220 young people from Western Romania voluntarily responded. By testing the hypothesis assuming the dynamic relationship between the two values through multiple regression analysis, the results demonstrate that in Model 1, which involves a linear relationship, health explains 42% of the variance in security with a F =161,215 significant at p <.01. In Model 2, which involves a curvilinear relationship, health explains 50% of the variance in security version with a F = 35,336 significant at p <.01. The incremental prediction optimization of 8% added by including the squared security accounts for the curve in the regression line, indicating the existence of a curvilinear relationship between security and health. The dynamic relationship demonstrates that upper limit and lower limit aspects of security, significantly influence the health value in a negative way. Normal levels of security trigger a high level of prioritization of health value. The implications of this type of relationship are discussed in explaining the value phenomenon at individual level. The study shows limits due to the selected sample, focusing exclusively on young people with higher education from the Western region of Romania. This sample was the target group of "The National Identity of Romanian Youth" project which designed this research.

A Smart Home is characterized by the presence of a huge number of small, low power devices, along with more classical devices. According to the Internet of Things (IoT) paradigm, all of them are expected to be always connected to the Internet in order to provide enhanced services. In this scenario, an attacker can undermine both the network security and the user’s security/privacy. Traditional security measures are not sufficient, because they are too difficult to setup and are either too weak to effectively protect the user or too limiting for the new services effectiveness. The paper suggests to dynamically adapt the security level of the smart home network according to the user perceived risk level what we have called network sentiment analysis. The security level is not fixed, established by a central system (usually by the Internet Service Provider) but can be changed with the users cooperation. The security of the smart home network is improved by a distributed firewalling and Intrusion Detection Systems both to the smart home side as to the Internet Service Provider side. These two parts must cooperate and integrate their actions for reacting dynamically to new and ongoing threats. Moreover, the level of network sentiment detected can be propagate to nearby home networks (e.g. the smart home networks of the apartments inside a building) to increase/decrease their level of security, thus creating a true in-line Intrusion Prevention System (IPS). The paper also presents a test bed for Smart Home to detect and counteract to different attacks against the IoT devices,,Wi-Fi and Ethernet connections .

In this paper, comprehensive datasets are presented to advance research on information security breaches. The datasets include data on disclosed information security breaches affecting S&P500 companies between 2020 and 2023, collected through manual search of the Internet. Overall, the datasets include 504 companies, with detailed information security breach and financial data available for 97 firms that experienced a disclosed information security breach. This document will describe the datasets in detail, explain the data collection procedure and shows the initial versions of the datasets.

The ability of deep learning to process vast data and uncover concealed malicious patterns has spurred the adoption of deep learning methods within the cybersecurity domain. Nonetheless, a notable hurdle confronting cybersecurity researchers today is the acquisition of a sufficiently large dataset to train deep learning models effectively. Privacy and security concerns associated with using real-world organization data have made cybersecurity researchers seek alternative strategies, notably focusing on generating synthetic data. Generative Adversarial Networks (GANs) have emerged as a prominent solution, lauded for their capacity to generate synthetic data spanning diverse domains. Despite their widespread use, the efficacy of GANs in generating realistic cyber attack data remains a subject requiring thorough investigation. Moreover, the proficiency of deep learning models trained on such synthetic data to accurately discern real-world attacks and anomalies poses an additional challenge that demands exploration. This paper delves into essential aspects of generative learning, scrutinizing their data generation capabilities, and conducts a comprehensive review to address the above questions. Through this exploration, we aim to shed light on the potential of synthetic data in fortifying deep learning models for robust cybersecurity applications.