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.

The rapid changes in technology on a global scale, combined with the widespread adoption of business operations in cyberspace, have intensified the need for robust protection against escalating risks posed by cyber threats. This research paper aims to identify fundamental cyber resilience management attributes that enable organizations to manage cybersecurity, sustain, and adapt amidst evolving cyber risks and threats. By integrating resilience theory and security theory, this study establishes the attributes for resilience within cyber domains, making a novel contribution to cyber resilience management in organizations. The study introduces a model featuring seven main variables: Rationale, Reliable, Readiness, Resistance, Robust, Rebound, Reflective, and sub-variables across the Physical, Logical, and Social cyber domains, providing a converged framework for achieving cyber resilience. The findings of the study highlight the significance of fundamental attributes for enhancing cyber resilience management in organizations, such as clarity in purpose, vision, and values for security management, an empowered culture, availability of resources, avoidance of single points of failure, development, and coordination of resources to respond to threats and risks, promotion of continual improvement, and the sharing of information and knowledge. In conclusion, this research paper presents a model for managing cybersecurity in organizations by identifying key attributes for achieving cyber resilience.

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.

In this COVID-19 pandemic, the use and dependency on Internet has grown exponentially. The number of people doing online activities such as e-learning, remote working, online shopping and others have increased. This has also led to increased vulnerability to cyber crimes. Cyber security attacks have become a serious problem. The common types of cyber security attacks are phishing, malware, ransomware, social engineering, identity theft and denial-of-service. The attackers target the victims in order to get their credential information or financial benefits. Those people who are doing online activities are vulnerable to cyber threats. This is because the network is not safe. The attackers are able to code according to the weaknesses of the Internet. Once the attackers hack into the devices, they have the root access and can do whatever they want to do with the device. In this research paper, the concept of cyber security attack and detailed research about real attacks are discussed. This is followed by detailed review about the recent cyber security attacks with a critical analysis. Moreover, the research paper will be proposing the latest research contribution of cyber security during COVID-19 and the implementation scenario which will give the examples about how the companies maintain privacy as well as the limitations. Then, the paper will be discussing the reasons that people are vulnerable to cyber security and the unique solution to the problems stated. Finally, this paper will conclude with an in-depth analysis and future direction for cyber security research.

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.

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.

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.

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.

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.

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats; malware, ransom-ware, social engineering, phishing etc. To prevent cyber-attacks one can use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, rganisations needed a strong base there are 5 types of cyber securities; Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time US is highly based on computers and on different software so it is really important for US to be more conscious about the security as they get many threats almost everyday for hacking their data and accounts.Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded on 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks so they try their best to take every precaution to prevent the hacking with double security and password manager tools.

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.

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.

The Internet of Things (IoT) continues to grow at a rapid pace, becoming integrated into the daily operations of individuals and organisations. IoT systems automate crucial services within daily life that users may rely on, which makes the assurance of security towards entities such as devices and information even more significant. In this paper, we present a comprehensive survey of papers that model cyber risk management processes within the context of IoT, and provide recommendations for further work. Using 39 collected papers, we studied IoT cyber risk management frameworks against four research questions that delve into cyber risk management concepts and human-orientated vulnerabilities. The importance of this work being human-driven is to better understand how individuals can affect risk and the ways that humans can be impacted by attacks within different IoT domains. Through the analysis, we identified open areas for future research and ideas that researchers should consider.

The increased adoption of information and communication technology for smart grid applications will require innovative cyber-physical system (CPS) testbeds to support research and education in the field. The groundbreaking CPS testbeds with realistic and scalable platforms have progressively gained interest in recent years, with electric power flowing in the physical layer and information flowing in the network layer. However, CPSs are critical infrastructures and not designed for testing or direct training, as any misbehaving in an actual system operation could cause a catastrophic impact on its operation. Based on that, it is not easy to efficiently train professionals in CPSs. Aiming to support the advancement and encourage the training of industry professionals, this paper proposes and develops a complete testbed with commercial tools. The testbed can reliably replicate the performance of smart grid systems and the main potential cyber threats that electric grids may face. The complex interdependencies between the cyber and physical domains are discussed in detail, and different case scenarios are presented, providing insightful guidelines for key features and design decisions for future smart grid testbeds.

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.

Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.

Artificial Intelligence (AI) is one of the key technologies of the Fourth Industrial Revolution (Industry 4.0), which can be used for the protection of Internet-connected systems from cyber-threats, attacks, damage, or unauthorized access. To intelligently solve today's various cybersecurity issues, popular AI techniques involving Machine Learning (ML) and Deep Learning (DL) methods, the concept of Natural Language Processing (NLP), Knowledge Representation and Reasoning (KRR), as well as the concept of knowledge or rule-based Expert Systems (ES) modeling can be used. Based on these AI methods, in this paper, we present a comprehensive view on "AI-driven Cybersecurity" that can play an important role for intelligent cybersecurity services and management. The security intelligence modeling based on such AI methods can make the cybersecurity computing process automated and intelligent than the conventional security systems. We also highlight several research directions within the scope of our study, which can help researchers do future research in the area. Overall, this paper's ultimate objective is to serve as a reference point and guidelines for cybersecurity researchers as well as industry professionals in the area, especially from an AI-based technical point of view.

As artificial intelligence continues to advance, researchers are increasingly using machine learning algorithms to study the factors that make people more susceptible to phishing scams. Most studies in this area have taken one of two approaches: either they explore statistical associations between various factors and susceptibility, or they use complex models such as deep neural networks to predict phishing behavior. However, these approaches have limitations in terms of providing practical insights for individuals to avoid future phishing attacks and delivering personalized explanations regarding their susceptibility to phishing. In this paper, we propose a machine learning approach that leverages explainable artificial intelligence techniques to examine the influence of human and demographic factors on susceptibility to phishing attacks. Our analysis reveals that psychological factors such as impulsivity and conscientiousness, as well as appropriate online security habits, significantly affect an individual's susceptibility to phishing attacks. Furthermore, our individualized case-by-case approach offers personalized recommendations on mitigating the risk of falling prey to phishing exploits, considering the specific circumstances of each individual.

In today’s Industrial IoT (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities, the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions against these attacks, as they have been proposed in the most recent literature.

As organizations are vulnerable to cyber attacks, their protection becomes a significant issue. Capability Maturity Models can enable organizations to benchmark current maturity levels against best practices. Although many maturity models have been already proposed in the literature, a need for models that integrate several regulations exists. This article presents a light web-based model that can be used as a cybersecurity assessment tool for Higher Education Institutes (HEIs) of the UK. The novel Holistic Cybersecurity Maturity Assessment Framework incorporates all security and privacy regulations and best practices that HEIs must be compliant to and can be used as a self-assessment or a cybersecurity audit tool.

A Wireless Body Area Network (WBAN) is a network of wirelessly connected sensing and actuating devices. WBANs used for recording biometric information and administering medication are classified as part of a Cyber Physical System (CPS). Preserving user security and privacy is a fundamental concern of WBANs, which introduces the notion of using biometric readings as a mechanism for authentication. Extensive research has been conducted regarding the various methodologies (e.g. ECG, EEG, gait, head/arm motion, skin impedance). This paper seeks to analyze and evaluate the most prominent biometric authentication techniques based on accuracy, cost, and feasibility of implementation. We suggest several authentication schemes which incorporate multiple biometric properties.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

This research article reports the results of a qualitative case study that correlates academic literature with five Industry 4.0 cyber trends, seven cyber risk frameworks and two cyber risk models. While there is a strong interest in industry and academia to standardise existing cyber risk frameworks, models and methodologies, an attempt to combine these approaches has not been done until present. We apply the grounded theory approach to derive with integration criteria for the reviewed frameworks, models and methodologies. Then, we propose a new architecture for the integration of the reviewed frameworks, models and methodologies. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of a holistic economic impact assessment model for IoT cyber risk.

Cyber-physical security is vital for protecting key computing infrastructure against cyber attacks. Individuals, corporations, and society can all suffer considerable digital asset losses due to cyber attacks, including Data loss, theft, financial loss, reputation harm, company interruption, infrastructure damage, ransomware attacks, and espionage. A cyber-physical attack harms both digital and physical assets. Cyber-physical system security is more challenging than software-level cyber security because it requires physical inspection and monitoring. This paper proposes an innovative and effective algorithm to strengthen Cyber-Physical Security (CPS) with minimal human intervention. It is a Human Activity Recognition (HAR)-based approach where a GoogleNet-BiLSTM network hybridization has been used to recognize suspicious activities in cyber-physical infrastructure perimeter. The proposed HAR-CPS algorithm classifies suspicious activities from real-time video surveillance with an average accuracy of 73.15%. It incorporates Machine Vision at the IoT Edge (Mez) technology to make the system latency tolerant. Dual-layer security has been ensured by operating the proposed algorithm and GoogleNet-BiLSTM hybrid network from a cloud server, which ensures the security of the proposed security system. The innovative optimization scheme makes it possible to strengthen cyber-physical security with $4.29 per month only.

Ransomware is one of the most dangerous types of malware, which is frequently intended to spread through a network to damage the designated client by encrypting the client’s vulnerable data. Conventional signature-based ransomware detection technique falls behind because it can only detect known anomalies. When it comes to new and non-familiar ransomware traditional system unveils huge shortcomings. For detecting unknown patterns and sorts of new ransomware families,behavior-based anomaly detection approaches are likely to be the most efficient approach. In the wake of this alarming condition, this paper presents an ensemble classification model consisting of three widely used machine learning techniques that include Decision Tree (DT), RandomForest (RF), and K-nearest neighbor (KNN). To achieve the best outcome ensemble soft voting and hard voting techniques are used while classifying ransomware families based on attack attributes. Performance analysis is done by comparing our proposed ensemble models with standalone models on behavioral attributes based ransomware dataset..

The ignorance of or lack of knowledge about cybersecurity aspects causes a critical problem regarding confidentiality and privacy. This security problem will continue to exist even if the user possesses less expertise in information security. The modern IT technologies are well developed, and almost everyone uses the features of IT technologies and services within the Internet. However, people are being affected due to cybersecurity threats. People can adhere to the recommended cybersecurity guidelines, rules, adopted standards, and cybercrime preventive measures. However, it is not possible to entirely avoid cybercrimes. Cybercrimes often lead to sufficient business losses and spread forbidden themes (hatred, terrorism, child porn, etc.). Therefore, to reduce the risk of cybercrimes, a web-based Blockchain-enabled cybersecurity awareness program (WBCA) process is introduced in this paper. The proposed web-based cybersecurity awareness program trains users to improve their security skills. The proposed program helps with understanding the common behaviors of cybercriminals and improves user knowledge of cybersecurity hygiene, best cybersecurity practices, modern cybersecurity vulnerabilities, and trends. Furthermore, the proposed WBCA uses the Blockchain technology to protect the model from the potential threats. The proposed model is validated and tested using real-world cybersecurity topics with real users and cybersecurity experts. We anticipate that the proposed program can be extended to other domains, such as national or corporate courses, to increase the cybersecurity awareness level of users.

Given the landscape of intricate and constantly evolving cyber threats, organizations demand refined strategies to deploy a Security Information and Event Management to support the management of a Cyber Security Operations Center. The dynamic nature of cyber threats complicates the efficient allocation of the location of network intrusion detection sensors, a critical component of a robust cybersecurity framework. Our research introduces an approach that integrates the precision of biomimetic optimization algorithms with the adaptability of Deep Q–Learning. By employing different biomimetic algorithms enhanced with deep learning, we aim to refine the deployment of sensors in network infrastructures, balancing the network security imperative against deployment costs. The results of computational tests demonstrate that the improved iterations leveraging Deep Q–Learning have outperformed their native counterparts. These findings underscore the importance of reinforcement learning, specifically through Deep Q–Learning, as a powerful tool to enhance the effectiveness of metaheuristics in addressing optimization challenges.

The world has changed. New technologies, new trends, new protocols, new efficient algorithms, and systems have been used widely by civilized countries to have better and more convenient living. In this research, there have been introduced many new terms, technologies, and mechanisms. Moreover, threats, attacks, and vulnerabilities have been studied and a novel scenario has been proposed in order to overcome all these violations that have been presented in this paper. The scenario is based on a security model that has been proposed for critical sectors, such as hospitals, industries, etc., which need efficient solutions to go one step further and improve the secure and efficient living and treatment of people. The aim of this research has been to understand through research the IoT security issues and propose the appropriate solutions based on the most suitable security algorithms. The results, which have been based on the complexity, the throughput, the power consumption, the strength, and the memory usage of the algorithms studied, have been hopeful and helpful for future research and experimentation.

This paper incorporates the definition of Intrusion Detection Systems and the methodologies utilised by these systems. As well as this, this research paper also encompasses a taxonomy and a survey of IDS and the specific strategies and principles. Finally, this paper also includes a discussion amongst other authors for instance what the authors differ and agree on, along with the previously related studies.

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.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

In recent years, groups of cyber criminals/hackers have carried out cyber-attacks using various tactics with the goal of destabilizing web services in a specific context for which they are motivated. Predicting these attacks is a critical task that assists in determining what actions should be taken to mitigate the effects of such attacks and to prevent them in the future. Although there are programs to detect security concerns on the internet, there is currently no system that can anticipate or foretell whether the attacks will be successful. This research aims to develop sustain-able strategies to reduce threats, vulnerability, and data manipulation of chatbots, consequently improving cyber security. To achieve this goal, we develop a conversational chatbot, an application that uses artificial intelligence (AI) to communicate, and deploy it on social media sites (e.g., Twitter) for cyber security purposes. Chatbots have the capacity to consume large amounts of information and give an appropriate response in an efficient and timely manner, thus rendering them useful in predicting threats emanating from social media. The research utilizes sentiment analysis strategy by employing chatbots on Twitter (and analyzing Twitter data) for predicting future threats and cyber-attacks. The strategy is based on a daily collection of tweets from two types of users: those who use the platform to voice their opinions on important and relevant subjects, and those who use it to share information on cyber security attacks. The research pro-vides tools and strategies for developing chatbots that can be used for assessing cyber threats on social media through sentiment analysis leading to a global sustainable development of businesses. Future research may utilize and improvise on the tools and strategies suggested in our research to strengthen the knowledge domain of chatbots, cyber security, and social media.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

In this paper we present an understanding of cyber risks in the Internet of Things (IoT), we explain why it is important to understand what IoT cyber risks are and how we can use risk assessment and risk management approaches to deal with these challenges. We introduce the most effective ways of doing Risk assessment and Risk Management of IoT risk. As part of our research, we also developed methodologies to assess and manage risk in this emerging environment. This paper will take you through our research and we will explain: what we mean by the IoT; what we mean by risk and risk in the IoT; why risk assessment and risk management are important; the IoT risk management for incident response and recovery; what open questions on IoT risk assessment and risk management remain.

In a computing context, cybersecurity is undergoing massive shifts in technology and its operations in recent days, and data science is driving the change. Extracting security incident patterns or insights from cybersecurity data and building corresponding data-driven model, is the key to make a security system automated and intelligent. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science. In this paper, we focus and briefly discuss cybersecurity data science, where the data is being gathered from relevant cybersecurity sources, and the analytics complement the latest data-driven patterns for providing more effective security solutions. The concept of cybersecurity data science allows making the computing process more actionable and intelligent as compared to traditional ones in the domain of cybersecurity. We then discuss and summarize a number of associated research issues and future directions. Furthermore, we provide a machine learning-based multi-layered framework for the purpose of cybersecurity modeling. Overall, our goal is not only to discuss cybersecurity data science and relevant methods but also to focus the applicability towards data-driven intelligent decision making for protecting the systems from cyber-attacks.

Computers have evolved over the years and as the evolution continues, we have been ushered into an era where high-speed internet has made it possible for devices in our homes, hospital, energy and industry to communicate with each other. This era is what is known as the Internet of Things (IoT). IoT has several benefits in the health, energy, transportation and agriculture sectors of a country’s economy. These enormous benefits coupled with the computational constraint of IoT devices which makes it difficult to deploy enhanced security protocols on them make IoT devices a target of cyber-attacks. One approach that has been used in traditional computing over the years to fight cyber-attacks is Intrusion Detection System (IDS). However, it is practically impossible to deploy IDS meant for traditional computers in IoT environments because of the computational constraint of these devices. In this regard, this study proposes a lightweight IDS for IoT devices using an incremental ensemble learning technique. We used Gaussian Naive Bayes and Hoeffding tree to build our incremental ensemble model. The model was then evaluated on the TON IoT dataset. Our proposed model was compared with other state-of-the-art methods proposed and evaluated using the same dataset. The experimental results show that the proposed model achieved an average accuracy of 99.98\%. We also evaluated the memory consumption of our model which showed that our model achieved a lightweight model status of 650.11KB as the highest memory consumption and 122.38KB as the lowest memory consumption.

The need for the most accurate and flexible system of revenue collection from internal sources has become a matter of extreme urgency and importance in e-governance. This need underscores the eagerness on the part of the Government to look for a new principle and policy of revenue collection or to become aggressive and innovative in the mode of collecting revenue from existing sources using the present system. The Boards of some Governments in Africa, even up to the moment are facing a lot of setbacks in performing their tasks due to the manual system of revenue collection from the public. This can be improved through an effective collection of revenue using the most accurate and flexible system. Tax is usually collected in the form of specific sales tax, general sales tax, corporate income tax, individual income tax, property tax and inheritance tax. Problems such as high cost of collection, fraud, underpayment, leakage in revenue, poor access to information, poor tracking of defaulters is at the increase. As a result of this, there is need to computerize the revenue collection system. Computerized systems have proven to introduce massive efficiencies and quick collection of revenue from the public. This research work demonstrates how to design and implement an automated system of revenue collection and how to maintain a secured database for collected tax information. This research delves into the study of how machine learning algorithms and Software-defined Networks improve the security of such automated systems.

This paper is focused on mapping the current evolution of Internet of Things (IoT) and its associated cyber risks for the Industry 4.0 (I4.0) sector. We report the results of a qualitative empirical study that correlates academic literature with 14 - I4.0 frameworks and initiatives. We apply the grounded theory approach to synthesise the findings from our literature review, to compare the cyber security frameworks and cyber security quantitative impact assessment models, with the world leading I4.0 technological trends. From the findings, we build a new impact assessment model of IoT cyber risk in Industry 4.0. We therefore advance the efforts of integrating standards and governance into Industry 4.0 and offer a better understanding of economics impact assessment models for I4.0.

The Internet of Things (IoT) is one of the most widely used technologies today, and it has a significant effect on our lives in a variety of ways, including social, commercial, and economic aspects. In terms of automation, productivity, and comfort for consumers across a wide range of application areas, from education to smart cities, the present and future IoT technologies hold great promise for improving the overall quality of human life. However, cyber-attacks and threats greatly affect smart applications in the environment of IoT. The traditional IoT security techniques are insufficient with the recent security challenges considering the advanced booming of different kinds of attacks and threats. Utilizing artificial intelligence (AI) expertise, especially machine and deep learning solutions, is the key to delivering a dynamically enhanced and up-to-date security system for the next-generation IoT system. Throughout this article, we present a comprehensive picture on IoT security intelligence, which is built on machine and deep learning technologies that extract insights from raw data to intelligently protect IoT devices against a variety of cyber-attacks. Finally, based on our study, we highlight the associated research issues and future directions within the scope of our study. Overall, this article aspires to serve as a reference point and guide, particularly from a technical standpoint, for cybersecurity experts and researchers working in the context of IoT.

One of the most burning topics in cybersecurity in 2023 will undoubtedly be the compliance with the Software Bill of Materials. Since the US president issued the Executive Order 14028 on Improving the Nation’s Cybersecurity, software developers have prepared and bills are transmitted to vendors, customers, and users, but they don’t know what to do with the reports they are getting. In addition, since software developers have identified the values of the Software Bill of Materials, they have been using the reports extensively. This article presents an estimate of 270 million requests per month, just from form one popular tool to one vulnerability index. This number is expected to double every year and a half. This simple estimate explains the urgency for automating the process. We propose solutions based on artificial intelligence and machine learning, and we base our tools on the existing FAIR principles (Findable, Accessible, Interoperable, and Reusable). This methodology is supported with a case study research and Grounded theory, for categorising data into axis, and for verifying the values of the tools with experts in the field. We showcase how to create, and share Vulnerability Exploitability eXchange data, and automate the Software Bill of Materials compliance process with AI models and a unified computational framework combining solutions for the following problems: (1) the data utilisation problem, (2) the automation and scaling problem, (3) the naming problem, (4) the alignment problem, (5) the pedigree, and provenance problem, and many other problems that are on the top of mind for many security engineers at present. The uptake of these findings will depend on collaborations with government and industry, and on the availability and the ease of use of automated tools.

Electric power systems are experiencing relevant changes involving the growing penetration of distributed generation and energy storage systems, the introduction of electric vehicles, the management of responsive loads, the proposals for new energy markets and so on. Such evolution is pushing for a paradigm shift: the management must move from traditional planning and manual intervention to full “smartization” of medium and low voltage networks. Peculiarities and criticalities of future power distribution networks originate from the complexity of the system that includes both the physical aspects of electric networks and the cyber aspects, like data elaboration, feature extraction, communication, supervision and control; only fully integrated advanced monitoring systems can foster this transition towards network automation. The design and development of such future networks require distinct kinds of expertise in the industrial and information engineering fields. In this context, this paper provides a comprehensive review of current challenges and multidisciplinary interactions in the development of smart distribution networks.

Foundational and state-of-the-art anomaly detection methods through power system state estimation are reviewed. The traditional components for bad data detection such as chi-square testing, residual-based methods, and hypothesis testing are discussed to explain the motivations for recent anomaly detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest change detection and artificial intelligence are discussed and directions for research are suggested with particular emphasis on considerations of the future smart grid.

Connected and Autonomous Vehicle (CAV) combines technologies of autonomous vehicle (AV) and connected vehicles (CV) to develop quicker, more reliable and safer traffic. Artificial Intelligence (AI) based CAV solutions play significant roles in sustainable city. The convergence imposes stringent security requirements for CAV safety and reliability. In practice, vehicles are developed with increased automation and connectivity. Increased automation increases the reliance on the sensor-based technologies and decreases the reliance on driver; increased connectivity increases the exposures of vehicles vulnerability and increases the risk for an adversary to implement a cyber-attack. Much work has been dedicated to identifying the security vulnerabilities and recommending mitigation techniques associated with different sensors, controllers, and connection mechanisms, respectively. However, there is an absence of comprehensive and in-depth studies to identify how the cyber-attacks exploit the vehicles vulnerabilities to negatively impact the performance and operations of CAV. In this survey, we set out to thoroughly review the security issues introduced by AV and CV technologies, analyze how the cyber-attacks impact the performance of CAV, and summarize the solutions correspondingly. The impact of cyber-attacks on the performance of CAV is elaborated from both viewpoints of intra-vehicle system and inter-vehicle system. We pointed out that securing the perception and operations of CAV would be the top requirement to enable CAV to be applied safely and reliably in practice. Also, we suggested to utilize cloud and new AI methods to defend against smart cyber-attacks on CAV.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

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.

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.

Financial technologies, stemming from the application of artificial intelligence to big data in finance, are continuously expanding, across different markets and financial services. While financial technologies bring many opportunities, such as reduced costs and extended inclusion, they also bring risks, among which cyber risks, which are constantly increasing and are difficult to measure. Among the difficulties in measurement lies the existence of interdependence among different cyber risks. The study of interdependence and possible contagion channels between cyber attacks to different institutions and economic sectors is indeed increasingly important to ensure economic and financial sustainability. Against this backdrop, this paper proposes a multivariate model for count time series of cyber risk events, in which the time-varying intensity parameter determining the probability that a cyber attack occurs evolves according to general autoregressive score models, taking both time and sectorial dependence into account. The model is particularly suitable for studying how the behaviors of different markets or sectors are interconnected and it constitutes a new approach to the multivariate analysis of count time series of cyber loss events.

We present an updated design process for adapting and integrating existing cyber risk assessment approaches for impact assessment for the risk from IoT to the digital economy. The new design process includes a set of changes to the original standards (e.g. NIST) that are adapted for the IoT cyber risk in this paper. This paper also presents a new framework for impact assessment of IoT cyber risk, specific for the digital economy.

The world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

The Internet-of-Things (IoT) enables enterprises to obtain profits from data but triggers data protection questions and new types of cyber risk. Cyber risk regulations for the IoT however do not exist. The IoT risk is not included in the cyber security assessment standards, hence, often not visible to cyber security experts. This is concerning, because companies integrating IoT devices and services need to perform a self-assessment of its IoT cyber security posture. The outcome of such self-assessment needs to define a current and target state, prior to creating a transformation roadmap outlining tasks to achieve the stated target state. In this article, a comparative empirical analysis is performed of multiple cyber risk assessment approaches, to define a high-level potential target state for company integrating IoT devices and/or services. Defining a high-level potential target state represent is followed by a high-level transformation roadmap, describing how company can achieve their target state, based on their current state. The transformation roadmap is used to adapt IoT risk impact assessment with a Goal-Oriented Approach and the Internet of Things Micro Mart model.

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.

Deep learning (DL), which is originated from an artificial neural network (ANN), is one of the major technologies of today's smart cybersecurity systems or policies to function in an intelligent manner. Popular deep learning techniques, such as Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN or ConvNet), Recurrent Neural Network (RNN) or Long Short-Term Memory (LSTM), Self-organizing Map (SOM), Auto-Encoder (AE), Restricted Boltzmann Machine (RBM), Deep Belief Networks (DBN), Generative Adversarial Network (GAN), Deep Transfer Learning (DTL or Deep TL), Deep Reinforcement Learning (DRL or Deep RL), or their ensembles and hybrid approaches can be used to intelligently tackle the diverse cybersecurity issues. In this paper, we aim to present a comprehensive overview from the perspective of these neural networks and deep learning techniques according to today's diverse needs. We also discuss the applicability of these techniques in various cybersecurity tasks such as intrusion detection, identification of malware or botnets, phishing, predicting cyber-attacks, e.g. denial of service (DoS), fraud detection or cyber-anomalies, etc. Finally, we highlight several research issues and future directions within the scope of our study in the field. Overall, the ultimate goal of this paper is to serve as a reference point and guidelines for the academia and professionals in the cyber industries, especially from the deep learning point of view.

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

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.

In the evolving landscape of cybersecurity threats, the Permanent Denial of Service (PDoS) attacks have emerged as a particularly damaging form of cyber aggression. Unlike the more well-known Denial of Service (DoS) attacks, which disrupt services temporarily, PDoS attacks aim to inflict irreversible damage to systems, often resulting in significant system overhauls and requiring hardware replacement. To enable the development of effective security measures, but also to address the knowledge gaps, this paper offers an in-depth exploration of PDoS attacks, emphasizing their distinguishing characteristics, underlying mechanisms, and potential further development. Through a comprehensive case study, the research highlights diverse tactics and strategies employed by attackers, from targeting IoT devices to manipulating boot processes and exploiting firmware vulnerabilities. A novel classification of PDoS attack vectors is proposed, that also explains the ways in which the systems can be compromised. The findings confirm the pressing need for adaptive and robust defense mechanisms to mitigate the threats posed by PDoS attacks in our interconnected digital world.

A groundbreaking integrated predictive model, termed Cyber Forensics Behavioral Analysis (CFBA), has been developed in an environment characterized by rapidly evolving and increasingly sophisticated cyber threats. This model merges Cyber Behavioral Sciences with Digital Forensics to enhance the prediction accuracy and effectiveness of cyber threats originating from specific Autonomous System Numbers (ASNs). It has been observed that traditional cybersecurity strategies, predominantly focused on technical aspects, must be improved in addressing the complex landscape of cyber threats. Consequently, a novel approach has been proposed in this research, combining technical expertise with insights into the behavior of cybercriminals, thereby addressing a significant gap in existing methodologies. A mixed-methods approach has been employed in the study, integrating quantitative and qualitative research methods. This approach has created a comprehensive framework incorporating digital forensics, cybersecurity, computer science, forensic psychology, and the cyber behavioral sciences which incorporates disciplines scuch as cyberpsychology and forensic cyberpsychology. The study is anchored around four key concepts: (1) Forensic Cyberpsychology, which focuses on understanding psychological aspects of cybercriminal behavior; (2) Digital Forensics, involving the collection and analysis of digital evidence from cyber incidents; (3) Predictive Modeling, which utilizes historical data and patterns to anticipate potential cyber threats; and (4) the Cyber Behavioral Analysis Metric (CBAM) and Cyber Behavioral Score (CBS), tools designed for evaluating and scoring ASNs based on their behavior in terms of cybersecurity threat risks. Challenges encountered in the development of this integration have been significant. Initial setbacks included the misalignment of traditional cyber defense methods with the evolving nature of cyber threats and the absence of a holistic approach in pre-existing models. Through the narrative of these challenges and the subsequent development of the CFBA model, the research highlights the necessity for an interdisciplinary approach to cybersecurity. The outcomes of this study are not merely theoretical but provide practical, actionable tools and frameworks that markedly improve the precision of predicting cyber threats, especially from ASNs. The CFBA model represents a significant shift in cybersecurity strategies, highlighting the importance of integrating behavioral insights with technical knowledge. The study emphasizes the need for ongoing research and collaboration in cybersecurity, advocating for an approach that is as comprehensive and multifaceted as the cyber threats it seeks to mitigate. This research has been conducted to contribute significantly to cybersecurity, offering new perspectives and methodologies in a rapidly changing domain.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

In various domains such as energy, manufacturing, and maritime, cyber-physical systems (CPS) have seen increased interest. Both academia and industry have focused on the cybersecurity aspects of such systems. The assessment of cyber risks in CPS is a popular research area with many existing approaches that aim to suggest relevant methods and practices. However, few works have addressed the extensive and objective evaluation of the proposed approaches. In this paper, a standard-aligned evaluation methodology is presented and empirically conducted to evaluate a newly proposed cyber risk assessment approach for CPS. The approach, which is called FMECA-ATT&CK is based on Failure Mode, Effects & Criticality Analysis (FMECA) risk assessment process and enriched with the semantics and encoded knowledge in the Adversarial Tactics, Techniques, and Common Knowledge framework (ATT&CK). Several experts were involved in conducting two risk assessment processes, FMECA-ATT\&CK and Bow-Tie, against two use cases in different application domains, particularly an autonomous passenger ship (APS) as a maritime use case and a digital substation as an energy use case. This allows for the evaluation of the approach based on a group of characteristics, namely, applicability, feasibility, accuracy, comprehensiveness, adaptability, scalability, and usability. The results highlight the positive utility of FMECA-ATT&CK in model-based, design-level, and component-level cyber risk assessment of CPS with several identified directions for improvements. Moreover, the standard-aligned evaluation method and the evaluation characteristics have been demonstrated as enablers for the thorough evaluation of cyber risk assessment methods.

The use of technologies and the Internet poses problems and risks related to digital security. This article presents the results of a study on the evaluation of the digital competence of future teachers in the DigCompEdu European framework. 317 undergraduate students from Spain and Portugal answered a questionnaire with 59 items, validated by experts, in order to assess the level and predominant competence profile in initial training (including knowledge, uses and interactions and attitudinal patterns). The results show that 47% of the participants belong to the profile of teachers at medium digital risk, evidencing habitual practices that involve risks such as sharing information and digital content inappropriately, not using strong passwords, and ignoring concepts such as identity, digital “footprint” and digital reputation. The average valuations of each item in the seven categories show that future teachers have an average competence in the area of digital security. They have good attitudes toward security but less knowledge and fewer skills and practices related to the safe and responsible use of the Internet. Future lines of work are proposed, aimed at responding to the demand for a better prepared and more digitally competent citizenry. The demand for education in security, privacy and digital identity is becoming increasingly important, and these elements form an essential part of initial training.

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.

The rapid integration of Information and Communication Technology (ICT) is transforming the traditional electrical grid into a {\em Smart Grid}. Smart grids enable two-way communication and improved monitoring and control between utilities and customers. However, due to its heterogeneous nature, public exposure, and weak security at low-powered devices, the Smart Grid has vulnerabilities to various malicious threats, adversaries, and cyber attacks, which may affect cost and service availability. Additionally, when the systems' confidentiality, integrity, or availability are compromised, the resulting fallout can threaten national security and have cascading effects on human lives. Given the extreme consequences of an attack, smart-grid technology must be thoroughly tested for correct operation and security {\em before} it is deployed. As a result, vulnerability testing of smart grids, not only for correctness but for security purposes, has been the subject of numerous studies by academics, government agencies, and private companies. This paper reviews the vulnerabilities associated with the smart grid and spotlights simulation as the vulnerability testing methodology conducted in recent pertinent research works. It also presents various security aspects of the smart grid, including grid applications, system and network infrastructure and components, cyber threats and attacks, simulation, and different mitigation techniques. Finally, we analyze the gaps in the current research works, focusing on simulation. We briefly present a real-time simulation testbed that mimics customer behaviour and integrates hardware in the loop to apply attack methods, analyze vulnerabilities and risk mitigation associated with the smart grid system, and propose future work to improve the current framework.

Phishing is a set of devastating techniques which lure target users to provide critical resources. They are successful because they rely on human weaknesses. Gamification which is a recent and non-traditional learning method with purpose to motivate and engage user to carry out activities, is more and more applied to prevent such cyber threats. This paper provides the first survey of gamified solutions dedicated to educate against phishing from 2007 to 2019. The investigation is conducted on eight proposals in terms of core concepts, game mechanics and learning process. We provide three taxonomies of dimensions to systematically characterize researches on gamified solutions, discuss lacks of surveyed works and opens further orientations to enhance this research area. Some key results are: solutions do not consider elementary level of knowledge and do no offer basic notions; solutions are not adapted to general audience and therefore not reliably applicable in different contexts; platforms partially educate about phishing; learners are evaluated predictably and within a short period. This study constitutes a cornerstone to understand and enhance research on phishing education.

The next-generation "Industrial Internet of Things" (IIoT) will support "Machine-to-Machine" (M2M) communications for smart Cyber-Physical-Systems and Industry 4.0, and require guaranteed cyber-security. This paper explores hardware-enforced cyber-security for critical infrastructures. It examines a Quantum-Safe "Software-Defined Deterministic IIoT" (SDD-IIoT), with a new forwarding-plane (sub-layer-3a) for deterministic M2M traffic flows. A "Software-Defined-Networking" (SDN) control-plane controls many "SDD Wide Area Networks" (SDD-WANs), realized with FPGAs. The SDN control-plane provides an "Admission-Control/Access-Control" system for network-bandwidth, using collaborating Artificial Intelligence (AI) rule-based "Zero Trust Architectures" (ZTAs). Hardware-enforced access-control eliminates all congestion, BufferBloat, and DoS/DDoS attacks in the forwarding-plane, reduces buffer-sizes by 100,000+ times, and supports ultra-reliable and uItra-low-latency communications in the SDD-WANs. The SDD-WANs can: (i) Encrypt/Authenticate M2M flows using Quantum-Safe ciphers, to withstand attacks by Quantum Computers; (ii) Implement "Guaranteed Intrusion Detection Systems" in FPGAs, to detect cyber-attacks embedded within billions of IIoT packets/second; (iii) Provide guaranteed immunity to external cyber-attacks against critical infrastructure, and exceptionally-strong immunity to internal cyber-attacks; (iv) Save $US100s of billions annually by exploiting FPGAs; and (v) Enable "Quantum Key Distribution" (QKD) Networks by providing a programmable forwarding-plane with "authenticated classical channels" and full-immunity to DoS/DDoS attacks. Extensive experimental results for an SDD-WAN over the European Union are reported.

The Internet-of-Things (IoT) triggers new types of cyber risks. Therefore, the integration of new IoT devices and services requires a self-assessment of IoT cyber security posture. By security posture this article refers to the cybersecurity strength of an organisation to predict, prevent and respond to cyberthreats. At present, there is a gap in the state-of-the-art, because there are no self-assessment methods for quantifying IoT cyber risk posture. To address this gap, an empirical analysis is performed of 12 cyber risk assessment approaches. The results and the main findings from the analysis is presented as the current and a target risk state for IoT systems, followed by conclusions and recommendations on a transformation roadmap, describing how IoT systems can achieve the target state with a new goal-oriented dependency model. By target state, we refer to the cyber security target that matches the generic security requirements of an organisation. The research paper studies and adapts four alternatives for IoT risk assessment and identifies the goal-oriented dependency modelling as a dominant approach among the risk assessment models studied. The new goal-oriented dependency model in this article enables the assessment of uncontrollable risk states in complex IoT systems and can be used for a quantitative self-assessment of IoT cyber risk posture.

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.

Technologies providing copyright-infringing IPTV content are a commonly used, as an illegal alternative to legal IPTV subscriptions and services, as they usually have lower monetary costs, and can be more convenient for the users that follow content from different sources. These infringing IPTV technologies may include websites, software, software add-ons, and physical set-top boxes. Due to the free or low cost of illegal IPTV technologies, illicit IPTV content providers will often resort to intrusive advertising, scams, and the distribution of malware to increase their revenue. We developed an automated solution for collecting and analysing malware from illegal IPTV technologies and used it to analyse a sample of illicit IPTV websites, application (app) stores, and software. Our results show that our IPTV Technologies Malware Analysis Framework (IITMAF) classified 32 of the 60 sample URLs tested as malicious, compared to running the same test using publicly available online anti-virus solutions, which only detected 23 of the 60 sample URLs as malicious. Moreover, the IITMAF framework also detected malicious URLs and files from 31 of the sample’s websites, one of which had reported ransomware behaviour.

This article delves deeply into digital forensics, covering computer forensics, network 1 forensics, and mobile device forensics. It analyzes the techniques and methodologies used by forensic 2 investigators in various disciplines. It underlines the diffculties investigators encounter and the 3 importance of thorough investigations to combat ever-increasing cyber risks. The paper emphasizes 4 the necessity of leveraging digital forensic tools to improve cybersecurity and provides a thorough 5 list of widely used Python libraries suitable for each investigation strategy, allowing for effective 6 comparison. Furthermore, it emphasizes the availability and suitability of these Python libraries in 7 computer device investigations (PyTSK3, Volatility, Pyregf, and Pyevtx), mobile device investigations 8 (Pytsk3, Volatility, Pyewf, dfVFS, Androguard, and pyMobileDevice), and network forensics (Scapy, 9 Bro/Zeek, Dpkt, pypcap, and NetworkX). The creation of these libraries recognizes the complexities 10 of digital crimes and the importance of applying modern techniques in forensic investigations. 11 Particularly, digital forensics plays an important role for healthcare providers because modern 12 medical devices produce, store, and transmit large amounts of patient and therapy information, 13 which could provide a forensic investigator with a treasure trove of potential digital evidence.

In recent years, due to their frequent use and widespread use, IoT (Internet of Things) devices have become an attractive target for hackers. As a result of their limited network resources and complex operating systems, they are vulnerable to attacks. Using a honeypot can, therefore, be a very effective way of detecting malicious requests and capturing samples of exploits. The purpose of this article is to introduce honeypots, the rise of IoT devices, and how they can be exploited by attackers. Various honeypot ecosystems will be investigated further for capturing and analyzing information from attacks against these IoT devices. As well as how to leverage proactive strategies in terms of IoT security, it will provide insights on the attack vectors present in most IoT systems, along with understanding attack patterns.

A smartphone plummeted from a stratospheric height of 36 km (~119,000 feet), providing a complete record of its rapid descent and abrupt deceleration when it hit the ground. The smartphone was configured to collect internal sensor data at high rates. We discuss the state-of-the-art of smartphone environmental and sensing capabilities at the closing of year 2020 and present a flexible mobile sensor data model. The associated open-source application programing interface (API) and python software development kit (SDK) used in this work is transportable to any hardware platform and operating system.

as the growth and popularity of technology has become simultaneous ascend in both impacts and numbers of cyber criminals thanks to the web. For many years, the organization has strived in ways of preventing any attacks from cyber-criminal with advanced techniques. Cybercriminals and intruders are developing a more advanced way to breach the security surface of an organization. Advanced Persistent Threats are also known as APT are new and a lot more sophisticated version for multistep attack scenarios that are known and are targeted just to achieve a goal most commonly undercover activities. this report, there will cover everything I know that tells us about APT with more word and brief explanations

Online identity-based theft is known to be one of the most serious and growing threats to victims, such as individuals and organizations, over the last 10 years due to the enormous economic damage these crimes have caused. The availability of personal information on the Internet has increased the chances of this cyber-crime. Online identity theft crime is the result of a combination of cyber-crimes on the one hand and lack of awareness and training of users on the other hand to protect personal data on the other. Education and awareness, which also contributes to early detection, is the strongest tool for consumers to safeguard themselves from online identity fraud. This paper provides a comprehensive explanation of online identity theft, the various approaches that thieves use to attack individuals and organizations and the types of fraud involved in this cyber-crime. The aim of this research is to evaluate the need for a reformulation of the concept of identity theft in order to be compatible with the evolution of behaviors and fraud.

In traditional databases, the entity resolution problem (which is also known as deduplication), refers to the task of mapping multiple manifestations of virtual objects to its corresponding real-world entity. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual object appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, even more challenging problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult---if not impossible---to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats.

The number of SIEM introduction is increasing in order to detect threat patterns in a short period of time with a large amount of structured/unstructured data, to precisely diagnose crisis to threats, and to provide an accurate alarm to an administrator by correlating collected information. However, it is difficult to quickly recognize and handle with various attack situations using a solution equipped with complicated functions during security monitoring. In order to overcome this situation, new detection analysis process has been required, and there is an effort to increase response speed during security monitoring and to expand accurate linkage analysis technology. In this paper, reflecting these requirements, we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats requirements. we design and propose profiling auto-generation model that can improve the efficiency and speed of attack detection for potential threats.

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.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

In a dynamic and rapidly changing world, customers’ often conflicting demands plus fluid economic requirements, often driven by geo-politics, have continued to evolve, out-striping the capability of existing production systems. With its inherent shortcomings, the traditional factory has proven to be incapable of addressing these modern-day manufacturing challenges. Recent advancements in Industry 4.0 have catalyzed the development of new manufacturing paradigms (or smart factory visions) under different monikers (e.g., Smart factory, Intelligent factory, Digital factory, Cloud-based factory etc.) would help fix these challenges. Due to a lack of consensus on a general nomenclature for these manufacturing paradigms, the term Future Factory (or Factory of the Future) is here used as a collective euphemism, without prejudice. The Future Factory constitutes a creative convergence of multiple technologies, techniques and capabilities that represent a significant change in current production capabilities, models, and practices. It is a data-driven manufacturing approach and system that harnesses intelligence from multiple information streams i.e., assets (including people), processes, and subsystems to help create new forms of production efficiency and flexibility. Serving both as a review monograph and reference companion, this paper details the meanings, characteristics, and technological underpinnings of the Future Factory. It also elucidates on the architectural models that guide the structured deployment of these modern factories with particular emphasis on three advanced communication technologies capable of speeding up advancements in the field. It not only highlights the relevance of communication between assets but also lays out mechanisms to achieve these interactions using the Administration shell. Finally, the paper also discusses the key enabling technologies that are typically embedded into bare bone factories to help improve their visibility, resilience, intelligence, and capacity, in addition to how these technologies are being deployed and to what effect. At the onset of the study, we were interested in developing a monograph which would serve as a comprehensive but concise review of general principles, fundamental concepts, major characteristics, key building blocks and implementation guidelines for the Future Factory within the overall context of the manufacturing ecosystem, in the age of Industry 4.0. Our hope is that this paper would enrich the extant literature on advanced manufacturing, help shape policy and research, and provide insights on how some of the identified pathways can be diffused into industry.

: 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.

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.

This paper presents a configuration of dual output single phase current source inverter with 6 switches for microgrid applications. The inverter is capable of delivering power to two independent set of loads of equal voltages or different voltages at the load end. The control strategy is based on Integral Sliding Mode Control (ISMC). The remote monitoring of the inverter is performed with cyber infrastructure. The cyber physical test bench is developed based on Reconfigurable I/O processor (NI MyRIO-1900) for control and monitoring of the inverter. The inverter prototype is tested in cyber physical test bench in laboratory conditions. The performance of the inverter is analyzed and monitored through the remote system. Also, the inverter is analyzed with different voltage conditions.

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.

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.

The practice of outsourcing tasks to a large group or community, typically through an open call on the internet or mass media, with the aim of harnessing collective intelligence, skill sets, and creativity to address a critical problem or achieve a specific goal is known as 'Crowdsourcing.' In criminal investigations, law enforcement agencies often employ this approach to gather information, tips, or leads from the public via social media platforms, online forums, or mobile applications. Crowdsourcing emerges as a potent tool for police investigations due to its capacity to swiftly reach a broad audience and uncover information that might otherwise remain unnoticed. Moreover, it fosters trust between law enforcement and the community, showcasing a collaborative effort to solve crimes.This chapter critically analyses the efficacy and inefficacy of employing crowdsourcing in erstwhile criminal investigations by drawing insights from prominent cases influenced by this method.

This study critically analyses the realities and experiences of gender-based harassment in cyberspace, and aims to unveil the shadows that shroud this phenomenon. It discloses the online spaces that disseminate detrimental attitudes towards women, despite their physical absence. Cyber violence has become a global issue and it causes significant economic and societal consequences. Recognizing and dealing with the adverse effects caused by demeaning cyber gender harassment is essential. The study raises questions to explore the kind of cyberbullying offences that are brought on by misogynistic inclinations in online environments and the experiences of the women who have gone through cyberbullying. This study uses semi-structured interviews and the IPA technique of the analysis of data to thoroughly examine the unique experiences of cyber harassment victims by applying a qualitative research approach. The study looks into various misogynistic cyber harassment offences and analyses women's accounts. Due to obstacles to justice, cyber violence and harassment replicate physical problems like spousal abuse and sexual harassment. A constant assault of intimidation and harassment results from the traditionally male-dominated character of cyberspace, which affects women's social, economic, and psychological well-being. Participants related horrifying tales of families' indifference and law enforcement officials' trivialization. Mental health problems increase isolation and prevent involvement in academic and professional activities. Women's well-being is exacerbated by societal blaming and secondary victimization. This brief analysis clarifies the intricacies of gender-based harassment in cyberspace and emphasizes the urgent need for efficient solutions to address this widespread issue.

Cyber-Physical Systems form the new backbone of digital ecosystems. Their design can be coupled with engineering activities to facilitate dynamic adaptation and (re-)configuration. Behavior-oriented technologies enable highly distributed and while coupled operation of systems. Utilizing them for digital twins as self-contained design entities with federation capabilities makes them promising candidates to develop and run highly functional CPS. In this paper we discuss mapping CPS components to behavior-based digital twin constituents mirroring integration and implementation through subject-oriented models. These models, inspired by agent-oriented system thinking can be executed and increase transparency at design and runtime. Patterns recognizing environmental factors and operation details facilitate configuration of CPS. Subject-oriented runtime support enable dynamic adaptation and federated use.

In current efforts to digitize manufacturing and move it into the fourth stage of the industrial revolution, a wide range of integration solutions is being considered to enable manufacturing to adapt to change. In transforming a factory into a self-organized, autonomous factory, companies are currently struggling with rapidly changing requirements and production factors, among other things. This is a particular problem for the human being as an actor within the factory, as the amount of new technologies and protocols increases the training effort. Proprietary interfaces of the control providers, a wide range of different communication protocols, complicate the understanding of the production processes, the evaluation and testability of new use cases and increase the danger of creating silos of knowledge as well as building collaboration barriers. As a solution to these problems, we propose an open software platform and define a way to model use case driven domain specific asset representation (DSA) that focuses on the human being and his needs for representing the factory in a way that it meets his requirements for the current production needs. We therefore conducted research on google scholar on human factors in industry 4.0 and used technologies as well as already existing platforms and their architecture.

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.

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.