Security Journal最新文献

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.

The protracted war against violent extremism provides plausible evidence about the ineffectiveness of purely militaristic approaches. The G-5 Sahel region witnessed an overall trend of mounting insecurity (2001–2017) despite a plethora of programs by multiple actors to defeat terrorism. Deterring rather than defeating terrorism gained momentum in the fourth wave of deterrence research. Contemporary Deterrence Theory (CDT) suggests a toolkit of punishment, denial and delegitimization to face asymmetric threats posed by violent non-state actors. The present article analyzes the US policy instruments and the upward pattern of terrorist attacks in the G5 Sahel states based on the data of global terrorism database (GTD). It also examines the concepts of ungoverned space and state fragility, which represent the bedrock of US intervention in the region. The study argues for a shift of US policy instruments in the Sahel from defeating to deterring terrorism based on the tenets of CDT. Much more attention should be diverted towards a nuanced approach that distinguishes between domestic and transnational extremist groups, and prioritizes robust homegrown delegitimization initiatives in conjunction with more inclusive political processes and socio-economic resilience programs.

Abstract

The region of Nigeria that produces oil is experiencing a surge in community-based vigilante violence, potentially escalating conflict dynamics and increasing fear of injury. The reason it matters is that neighborhood vigilante groups are more likely to participate in criminal, political, and ethnic plotting and are not always controllable. This prompted us to look into whether GMoU cluster interventions by MOCs could lower the heat map of fatalities from vigilante insurgency in the Niger Delta of Nigeria. The results of logit regression and propensity score matching demonstrate that the MOCs' limited CSR efforts to protect the area have been successful in creating, formalizing, equipping, and managing vigilante groups. The results also show that the CSR initiatives have reduced vigilante violence within and between host communities, as well as violence against their residents. This implies that raising awareness of CSR with the goal of strengthening vigilante control will strengthen the local security apparatus, discourage resurgence in the various rural areas, safeguard the workers and equipment of oil firms, and provide a favorable business environment in the area.

Email-based fraud is a lucrative market for cybercriminals to scam a wide range of potential victims. Yet there is a sometimes conflicted literature on who these victims are, complicated by low and possibly confounded reporting rates. We make use of an experimental automated scam-baiting platform to test hypotheses about the characteristics online fraudsters find more attractive, gathering behavioural evidence directly from the fraudsters themselves (n = 296). In our comparison of four instrumented ‘personalities’ designed based on traits highlighted in the literature and in a small public perception survey, we find that a script adopting the personality of an elderly woman attracts significantly more engagement from scammers than our control measure. We discuss our approach and the possible interpretations and implications of our findings.

Fear of crime refers to the fear of being a victim of crime, and it is nurtured by folklore and the media. Folklore fosters fear of crime using oral tradition transferred throughout generations by updating old contexts into modern times. On the other hand, the media visualizes the possibility of being a victim by introducing the real-life experiences of victims. Contrary to folklore, victims presented in the media seem more accessible, and the victimization of individuals may be proven. Folklore and the media have cooperated to keep the fear of crime alive to warn society that individual lives are on the verge of becoming victims of criminals. In this paper, I aim to describe the fear of crime through two particular crimes: Child abductions and organ thefts. Data for this research comes from two sets of materials: Newspaper news stories and semi-structured interviews conducted in face-to-face settings with mothers from two different generations. My research reveals that mothers of the eighties were scared of their children being victims of beggar gangs and organ trade criminals, while millennial mothers suffer from the possibility of their children being victims of online grooming and sextortion.