Security Journal最新文献

The relevance of this topic is due to the increase in the level of crime in Kazakhstan, as well as in foreign countries amid the global crisis, inflation, and other aspects that affect the criminal behavior of citizens. The purpose of the work within this topic is to study the main aspects underlying the legal activities of entities authorized to apply measures to prevent and combat offences. These are the main methods used in this article: comparative method, statistical method, and method of systematization. The identification of the main regulatory legal acts of the Republic of Kazakhstan (RK) are the results of the work, which regulates the activities of internal affairs authorities in the relevant area; it has clarified the issue of problems that arise when performing the functions for prevention of offences among minors, as well as violations of legislation in the transport sector, and others. The most effective types and methods of preventing and deterring offences have been investigated. In addition, in the course of conducting research, it was analyzed foreign experience in the prevention and deterrence of several types of criminals, administrative, and other offences and the main achievements in the relevant field.

Gun violence significantly threatens tens of thousands of people annually in the United States. This paper proposes a multidisciplinary approach to address this issue. Specifically, we bridge the gap between criminology and computer vision by exploring the applicability of firearm object detection algorithms to the criminal justice system. By situating firearm object detection algorithms in situational crime prevention, we outline how they could enhance the current use of closed-circuit television (CCTV) systems to mitigate gun violence. We elucidate our approach to training a firearm object detection algorithm and describe why its results are meaningful to scholars beyond the realm of computer vision. Lastly, we discuss limitations associated with object detection algorithms and why they are valuable to criminal justice practices.

Dark Web has turned into a platform for a variety of criminal activities, including weapon trafficking, pornography, fake documents, drug trafficking, and, most notably terrorism as detailed in this study. This article uses an LDA-based topic modeling approach to identify the topics addressed in discussions on the Dark Web. The main purpose is to present an overview of jihadists’ communication in cyberspace for the detection of unusual behavior or terrorism-related purposes. According to the findings, conversations in the context of recruitment and propaganda predominated at the forum. There was no direct evidence of terrorist collaboration at the conclusion of the investigation. This does not, however, imply that these sites are risk-free. Propaganda and recruitment tools feed the terrorist activities.

This research is dedicated to unpacking the complex dynamics of disruptive behavior among youths in shopping malls. It leverages discursive psychology to dissect eight semi-structured interviews with security staff from Czech retail complexes. The study reveals that social control mechanisms within these semi-public spaces significantly impact young people’s conduct. Contributory factors to the onset and progression of unruly behavior are identified as Wi-Fi availability, which seeds the environment for practical jokes, peer pressure, and a lack of parental oversight. Although security measures tend to conform to formal protocols, they frequently rely on instinct or adopt an informal, parental approach. The research suggests that partnering with youth workers could provide a more effective strategy for addressing such behaviors, bridging the gap between formal policy and practice.

The surge of interest in digital currencies has led to a corresponding global increase in Ponzi schemes associated with cryptocurrencies. Scammers of cryptocurrency Ponzi schemes often target older adults who are cash-rich and have limited knowledge of digital assets. Therefore, to reduce the number of victims, it is imperative to increase the understanding of the crime itself. However, there remains a paucity of scholarly exploration focused on the criminal mechanisms underpinning such schemes and the strategic decision-making process of the perpetrators. This study seeks to address this gap in the literature and enhance the current understanding of cryptocurrency-based Ponzi schemes through an examination of their operational tactics. The methodology employed in this study distinguishes the progression of these schemes into two stages: the pre-crime phase and the criminal event phase, each scripted for further clarity, yielding three scripts for the former and eleven for the latter. Based on these parsed narratives, this research proposes several policy strategies aimed at curtailing the prevalence of cryptocurrency Ponzi schemes.

This study aims to reveal the competence areas and skill sets needed in the business world in the field of cyber security (CS). For this purpose, descriptive analysis, topic modeling analysis, and semantic content analysis were conducted on 9407 CS job advertisements obtained from Indeed.com. The results of the study revealed a total of 10 job titles and 23 skill sets expected by the business world in CS job postings. The first three titles in terms of volume were “Engineer”, “Analyst” and “Specialist”, while the first three skill sets were determined as “Business/Customer services”, “System engineering”, and “Bachelor degree”. In addition, maps showing the relationships between titles and skills were created with a title-skill set matrix. The results of our study can be expected to contribute to candidates and professionals in the field of cyber security, IT organizations, and educational institutions in the cyber security business world by seeing, evaluating, developing, and expanding the current knowledge, skills, and competencies needed in the field.

Crime is a socioeconomic problem that affects the quality of life and economic growth of a country, and it continues to increase. Crime prevention and prediction are systematic approaches used to locate and analyze historical data to identify trends that can be employed in identifying crimes and criminals. The objective of this study is to predict the type of crime that occurred in the city and identify the important features that make this prediction using a machine learning technique. For this experimental investigation, a supervised learning method was used to classify the types of crimes based on the final labelled class that indicates which type of crime is committed. Thus, decision tree (DT), random forest (RF), and K-nearest neighbor (KNN) algorithms are utilized along with the Python programming language in the Jupyter notebook environment. A total of 1400 records and nine attributes were used, and the data were split into training and testing sets, with 80% allocated to training and 20% for testing. The decision tree achieved an accuracy score of 84%, followed by the random forest at 86.07% and K-nearest neighbor at 81%. Besides this, the job of the offender, the victim’s age, and the offender’s age are the important features that cause crime. Therefore, it can be concluded that the use of machine learning to analyze historical data and the random forest algorithm to classify crimes yields promising results in predicting the type of crime.

Enterprise Security Risk Management (ESRM) is gaining popularity in industry circles, especially after the American Society of Industrial Security (ASIS International) elevated it as its strategic priority in 2016. However, research on its adoption has attracted little attention, especially in universities which are often characterized by outstanding variations in culture, structure, and more. In this paper, we conduct a self-assessment of ESRM maturity in Kenya’s accredited universities using process metrics of the 2019 ASIS ESRM Maturity Model and insights from university security executives. The findings reveal that more than 35% of accredited universities have achieved advanced levels of ESRM adoption, with over 57% at average or middle levels, predominantly at Level 3. Public accredited universities exhibit higher ESRM adoption levels compared to their private counterparts. The study also identifies variations in the terminology used, with 60% using “Security Risk Management (SRM),” 35% using “University Risk Management,” and a minority adopting ESRM. The discomfort with the “enterprise” term indicates a need for awareness and sensitization programs. We argue that benchmarking with optimized ESRM adopters and increasing awareness and integration of ESRM in strategic planning and institutional governance are crucial for comprehensive security risk management in higher education.