Dogs are considered property under UK law, while current discourses of pet ownership place canine companions as part of an extended family. This means sentences for those who steal dogs are not reflective of a dogs’ sentience and agency, rather reflecting the same charges for those who steal a laptop or wallet. This is particularly problematic as dog theft is currently on the rise in England and Wales and led to public calls to change the law. Recognizing that a more robust analysis of dog theft crime statistics is required, we gathered dog theft data for 2015, 2016 and 2017 from 37 of 44 police forces through FOI requests. This paper uses this data to examine how dog theft crime statistics are constructed; assesses the strengths and weaknesses of this data; and categorizes, maps and measures dog theft changes temporally per police force in England and Wales. Our findings reveal there has been an increase in dog theft crimes, 1,294 in 2015, 1,525 in 2016 (+17.85%), and 1,678 in 2017 (+10.03%); and a decrease in court charges related to dog theft crimes, 62 (4.7%) in 2015, 48 (3.14%) in 2016, 37 (2.2%) in 2017. There were police force inconsistencies in recording dog theft crime which meant some data was unusable or could not be accessed or analysed. There is a need for a qualitative study to understand dog theft crime in different areas, and standardised approach to recording the theft of a dog by all forces across England and Wales.

As technology evolves, our digital footprint keeps increasing. Now, everything we do is largely related to us being online, connected and logged. Therefore, if someone’s online data is exposed, then their life will be compromised. This is called identity theft and in this era of technology, it has become one of the most serious forms of crime. When someone’s data is compromised, there is a huge chance of harming the person financially, morally and socially. New technology and the knowledge gap it creates when it first comes out is perfect for identity thieves to exploit. If we are not cautious, our future will be very different than what we dream of due to the very technology created to make it better. Hence, we need to understand the technology, identify the loopholes and educate ourselves to avoid being held captive by our digital footprint.

This paper aims to analyze the role of natural heritage cataloging when criminal events challenge natural history museums. This study investigates the rhino horn thefts in Italian natural history museums from 2011 to 2015 to provide a comprehensive framework for these robberies while highlighting the weaknesses and strengths to prevent thefts and safeguard both rhino horns and natural history collections from future targeting

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.

Counterfeiting and theft have always been problems that incur high costs and results in considerable losses for the international markets. In this research paper, we will address the issue of counterfeiting while using RFID technology in retailer systems or other industries by presenting a new anti-counterfeiting and anti-theft system for the retailer market. This system will address the two above mentioned issues and provide a solution that can save the retailer systems millions of dollars yearly. This proposed system will achieve the objective of preventing or minimising the counterfeiting and theft of tagged products. At the same time, it will provide a strong indication for suspiciously sold or obtained items. Furthermore, we conducted a security analysis to prove the correctness of our protocol on the basis of the strand spaces.

Since the most important constituent of the cost of cryptocurrency production is energy bills, use of illegal electricity in cryptocurrency mining farms is very common. For this reason, many countries prohibit cryptocurrency production or restrict it with some legal regulations. This situation raises the problem of how to detect illicit cryptocurrency mining. Therefore, innovative approaches are needed to detect illicit cryptocurrency mining farms. In this study, it has been shown that unique noise and/or harmonic characteristics of machines producing cryptocurrency can be used to detect illicit cryptocurrency mining farms. The unique harmonic characteristics formed by data centers on neutral line have been detected by field measurements performed. Electricity distribution companies can make risk assessments with this approach by monitoring energy quality data. As a result, the necessary legal permissions can be obtained to investigate suspicious high-risk consumers. Illicit cryptocurrency mining farms can be detected by investigating at high-risk consumer facilities.

This study addresses the escalating threat of data theft ransomware, a form of cyberattack that exfiltrates sensitive information from victim networks and demands ransom for its non-disclosure. Unlike traditional crypto-ransomware, data theft ransomware leverages various infiltration techniques to gain unauthorized access and control over critical data assets. The study introduces a novel Moving Target Defense (MTD) framework, specifically designed for the Windows operating system environment, to counter these sophisticated attacks. MTD increases the unpredictability of the system by dynamically altering its configurations, thereby disrupting the adversary's ability to execute successful attacks. Our research includes the development and empirical evaluation of the MTD framework, demonstrating its effectiveness in reducing ransomware operational capabilities through quantitative analysis, qualitative observations, and statistical significance testing. We also explore the challenges and complexities involved in implementing MTD in real-world scenarios and propose strategies to overcome these barriers. The study concludes with recommendations for future research directions and the potential of MTD in enhancing cybersecurity defense mechanisms.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an on-going theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT), Cognitive Internet of Things, Internet of Medical Things, and Cloud Computing are expanding smart home concepts and solutions, and their applications. The primary objective of the present research work was to design and develop IoT and cloud computing based smart home solutions. In addition, we also propose a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision facility. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time.