Security Professionals Research Articles

Disaster Risk Reduction Education Through Digital Technologies in the Context of Education for Sustainable Development: A Curricula Analysis of Security and Defense Studies in Serbia

This study examines the integration of disaster risk reduction (DRR) into security and defense studies curricula at Serbian universities, focusing on public and private institutions. As climate change accelerates and natural disasters become more frequent, addressing these risks is critical for national security and sustainable development. This research evaluates the extent of DRR incorporation in curricula and the use of emerging technologies in DRR education. A qualitative analysis of programs at institutions such as the Faculty of Security Studies at the University of Belgrade, the Military Academy, the University of Criminal Investigation and Police Studies, and private universities like Singidunum and Educons University reveals that public institutions have made significant progress. However, private universities still need comprehensive DRR-focused courses and technological integration. This study recommends fostering collaboration between public and private universities, expanding access to the National Simulation Center, and incorporating modern technologies and active learning strategies across curricula to bridge existing gaps. These steps equip future security professionals with the practical skills and interdisciplinary knowledge necessary for effective disaster management in an increasingly complex risk environment.

Train one and get a crew: a multilevel evaluation of team-centric leadership training for airport security screening crew leaders

PurposeThis study aims to introduce a team-centric leadership training program tailored for leaders of critical action teams. It emphasizes the critical role of effective teamwork in high-stakes environments.Design/methodology/approachThe authors developed a team-centric leadership training program for crew leaders of airport security teams. The training program comprised in-class teaching on the psychological foundations of teamwork and practical simulations. The training program’s effectiveness was assessed at the individual and team levels using Kirkpatrick’s evaluation model to obtain a complete evaluation of its effectiveness. First, a pre- and posttraining questionnaire recorded changes in self-efficacy, knowledge and skills about teamwork at the individual level from 62 leaders. Second, the authors assessed the transfer effects of the team-centric leadership training on self-reported team processes and team performance during a work shift of 37 crews.FindingsResults indicate an improvement in the application of teamwork skills but not in self-efficacy or knowledge among crew leaders who underwent the training. Additional analysis revealed that leaders with initially high self-efficacy in teamwork experienced a reduction posttraining, whereas those with initially low levels reported an increase. Teams led by trained leaders demonstrated a higher engagement in key team processes than those led by untrained leaders. No effect has been found on team performance.Originality/valueThis study offers a cost-effective alternative to conventional teamwork training methods, specifically designed for high-pressure security contexts. The authors advocate for the adoption of this training model by security professionals, aiming to elevate team processes and operational efficacy in critical action teams.

Competencies for Privacy and Security Professionals: The Discrepancy in Perspective between Employers and Employees

Competencies for Privacy and Security Professionals: The Discrepancy in Perspective between Employers and Employees

Cracking the Core: Hardware Vulnerabilities in Android Devices Unveiled

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices.

Suspicious activity reporting in emerging economies: the case of Iran

PurposeCountries with high levels of corruption can experience collusion between powerful elites and accountants to conceal, disguise and clean the proceeds of criminality. This study investigates the willingness of accountants to report evidence of money laundering in an emerging economy, Iran, notwithstanding potential personal and professional risks implicit in such due diligence. It evaluates the relevance of personal characteristics of accountants to the propensity to report, and the implications for policy makers in terms of audit team composition.Design/methodology/approachThe methodology is quantitative. Data was gathered by means of a suspicious activity scenario-based questionnaire administered to 1,128 of Certified Public Accountants in Iran, of which 281 responses were received. Four hypotheses were tested relating to the implications, if any, of gender, age, education and working experience for the propensity to report red flags indicative of money laundering.FindingsData revealed that accountants were generally more willing to report activity indicative of money laundering than was anticipated in an environment perceived to be characterised by professional and personal risks. Older accountants are more risk averse and more likely to report suspicious activity than younger counterparts who tend to disregard borderline indicators of money laundering. A significant red flag indicator of money laundering is a client's reluctance to provide information regarding controlling shareholders, debtors and creditors or to explain contrived and opaque corporate structures. Audit teams may be more effective when gender-balanced: female accountants tend to be more willing to report suspicious activity than male counterparts, reducing the risk of interference by powerful elites.Research limitations/implicationsThe time frame over which the research was conducted was a single year; if it had been conducted over several years it may have revealed more nuanced and evolving reporting behaviour. The study was limited to Iran: a cross-comparison with another emerging economy or economies may have revealed useful contrasts.Originality/valueThe study contributes to behavioural accounting research in emerging economies. Limited empirical data is available regarding the influence of personal characteristics of accountants on their willingness to report suspicious activity in corrupt environments where personal safety and professional security may be at risk from powerful elites. It evaluates the implications of these for suspicious activity reporting policy, and for improving the effectiveness of the scrutineering role of audit teams. An innovative questionnaire was designed which may be suitable for future comparable research in emerging economies.

Field Trial of a Queue-Managing Security Guard Robot

We developed a security guard robot that is specifically designed to manage queues of people and conducted a field trial at an actual public event to assess its effectiveness. However, the acceptance of robot instructions or admonishments poses challenges in real-world applications. Our primary objective was to achieve an effective and socially acceptable queue-management solution. To accomplish this, we took inspiration from human security guards whose role has already been well received in society. Our robot, whose design embodied the image of a professional security guard, focused on three key aspects: duties, professional behavior, and appearance. To ensure its competence, we interviewed professional security guards to deepen our understanding of the responsibilities associated with queue management. Based on their insights, we incorporated features of ushering, admonishing, announcing, and question answering into the robot’s functionality. We also prioritized the modeling of professional ushering behavior. During a 10-day field trial at a children’s amusement event, we interviewed both the visitors who interacted with the robot and the event staff. The results revealed that visitors generally complied with its ushering and admonishments, indicating a positive reception. Both visitors and event staff expressed an overall favorable impression of the robot and its queue-management services. These findings suggest that our proposed security guard robot shows great promise as a solution for effective crowd handling in public spaces.

An Investigation into the Utilisation of CNN with LSTM for Video Deepfake Detection

Video deepfake detection has emerged as a critical field within the broader domain of digital technologies driven by the rapid proliferation of AI-generated media and the increasing threat of its misuse for deception and misinformation. The integration of Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) has proven to be a promising approach for improving video deepfake detection, achieving near-perfect accuracy. CNNs enable the effective extraction of spatial features from video frames, such as facial textures and lighting, while LSTM analyses temporal patterns, detecting inconsistencies over time. This hybrid model enhances the ability to detect deepfakes by combining spatial and temporal analysis. However, the existing research lacks systematic evaluations that comprehensively assess their effectiveness and optimal configurations. Therefore, this paper provides a comprehensive review of video deepfake detection techniques utilising hybrid CNN-LSTM models. It systematically investigates state-of-the-art techniques, highlighting common feature extraction approaches and widely used datasets for training and testing. This paper also evaluates model performance across different datasets, identifies key factors influencing detection accuracy, and explores how CNN-LSTM models can be optimised. It also compares CNN-LSTM models with non-LSTM approaches, addresses implementation challenges, and proposes solutions for them. Lastly, open issues and future research directions of video deepfake detection using CNN-LSTM will be discussed. This paper provides valuable insights for researchers and cyber security professionals by reviewing CNN-LSTM models for video deepfake detection contributing to the advancement of robust and effective deepfake detection systems.

Spor Lesů České republiky, s. p., se správními orgány o včasnou reakci na kůrovcovou kalamitu

The bark beetle calamity that hit the Czech Republic has been reflected, among other things, in the approach of administrative authorities and courts reviewing administrative acts. The article is an expert report that provides information on the judgment of the Municipal Court in Prague, which reflects the current approach of the courts to the assessment of offences consisting in neglect of forest care during the bark beetle calamity. The example of the state entity shows how this entity uses technical and professional security in the case of forest care during a bark beetle calamity. By analyzing the court decision, it was concluded that it is necessary to strictly comply with the obligations of the forest manager or owner to ensure forest protection against bark beetle calamity, but it should not become a precedent against small owners who do not have the personnel and technical capabilities of large forestry companies. According to the conclusions, it is evident that the court decision imposes high requirements on the forest owner, which the small owner is not able to meet. The article invites a discussion on whether the state should take a more active role in measures against bark beetle calamities.

A context-aware zero trust-based hybrid approach to IoT-based self-driving vehicles security

With the speedy progression and adoption of IoT devices in modern self-driving vehicles (SDVs), autonomous driving industry is gradually reforming its capabilities to provide better transportation services. However, this domain faces enormous security and privacy challenges and thus has become an attractive target for attackers due to its rapid growth and market worth. Furthermore, the rapid transformation in technological tools in transport industry and speedy evolution of cyber-attacks paved the way for designing efficient IDSs. Motivated by these challenges, we put forward a new secure and efficient IDS approach for the security of SDVs. The propose approach utilizes an emerging strategy to mitigate security vulnerabilities and cyber attacks detection using zero trust (ZT) model. Through this work, we put forward a context-aware zero trust security framework for IoT-based SDVs. The proposed framework utilizes a context-aware design to evaluate the trustworthiness of the devices using multi-source trust and reputation model. Then, to make the framework more effective and reliable, we introduce crawler system into the context of the IoT-devices in SDVs to make the system unbiased. Additionally, an observer module is developed that employs state-of-the-art machine learning algorithm to detect malicious actions. Empirical results on two standard benchmark datasets (i.e., Car Hacking and ToN_IoT) validate the practicality and robustness of propose framework in real-world transport systems with enhanced security and trust management against evolving cyber-threats. Detection results demonstrate that the proposed framework secured the best performance by achieving 99.43% and 99.52% accuracy for Car Hacking and ToN_IoT, respectively. The findings of this study will help the security professionals and researchers to comprehend the importance of ZT architecture in developing effective and robust security solutions for modern IoT-based SDVs.

Security Trends in Internet-of-things for Ambient Assistive Living: A Review

Abstract: The Internet of Things (IoT) has revolutionized our society and become indispensable to modern existence. The IoT allows users to access their electronic gadgets from any location. The widespread adoption of IoT across sectors, from manufacturing to surveillance to elder care, has contributed to its rising profile. New security risks and challenges arise with the growth of the IoT. With the development of IoT, the likelihood of an attack by hackers has increased. The burden of addressing these dangers falls on researchers and security professionals. This article looks into the challenges of IoT security in a real-world Ambient Assisted Living (AAL) environment. This work discusses the numerous security attacks employed by cybercriminals in AAL IoT. In addition, this research investigates the varied responses to the risks. We discussed the state-of-the-art technologies available for protecting AAL IoT networks. This work analyses and compares the majority of the latest technologies available. In conclusion, we offer a few suggestions for where the field could go from the current scenario

Ensuring the integrity assessment of IoT medical sensors using hesitant fuzzy sets.

Objective: The Internet of Medical Things (IoMT) is transforming healthcare systems, but concerns about device integrity and sensitive data are growing. The study aims to develop a framework for evaluating and prioritizing integrity schemes in healthcare for IoT-based medical sensor devices, addressing the challenges of selecting the right authentication solution due to its complexity and intricacy. Methods: A unified health-hesitant fuzzy expert system for IoMT sensor integrity assessment in Saudi Arabia is described in this paper. Medical sensor integrity literature and professionals are contacted first. Delphi is used to gather attributes of integrity approaches while an Internet of Things medical sensor integrity specialist supervises the operation. After collecting characteristics, good assessment criteria are created and the hesitant fuzzy analytic network procedure is used to assess integrity. Results: Functional integrity and measurement accuracy are the biggest factors in IoMT sensor security and integrity, according to assessment. The framework achieves 93%, 94%, and 95% precision, accuracy, and recall compared to current approaches. The framework helps healthcare integrity security professionals and stakeholders assess and resolve IoT medical sensor authentication issues. Conclusion: This health-hesitant fuzzy expert system will let Saudi Arabian and international healthcare stakeholders safely deploy IoMT sensors in the changing healthcare landscape.

Secure Efficiency: Navigating Performance Challenges in Multi-Tenant Cloud Security Implementations

Multi-tenant cloud environments offer significant advantages in terms of cost-efficiency and scalability, but they also present unique challenges in balancing robust security measures with optimal performance. This article examines the intricate relationship between security implementations and system performance in shared cloud infrastructures. Through a comprehensive analysis of data isolation techniques, access control mechanisms, network security protocols, and threat detection systems, we identify key areas where security measures can impact performance metrics such as latency, throughput, and resource utilization. Our article employs a mixed-methods approach, combining quantitative performance measurements with qualitative case studies from industry-leading cloud service providers. The findings reveal that while stringent security measures often introduce performance overhead, strategic implementation and optimization can significantly mitigate these effects. We propose a framework for dynamically balancing security and performance requirements, incorporating emerging technologies such as AI-driven threat detection and automated resource allocation. This article contributes to the growing body of knowledge on cloud computing optimization and provides practical insights for cloud architects and security professionals seeking to enhance both the security posture and performance efficiency of multi-tenant cloud environments.

A role played by the All-Union Communist Party (Bolsheviks) Central Committee Bureau for Lithuania in suppressing resistance of partisans (November 1944–March 1947)

By a decision of the Organisational Bureau of the All-Union Communist Party (Bolsheviks) Central Committee, Mikhail Suslov was dismissed as chairman of the ACP(B) CC Bureau for Lithuanian Affairs and appointed a member of the Organisational Bureau, which had an impact on changes in the distribution of power within the Communist Party of Lithuania and in its relations with Moscow. The changes in the composition of the Bureau for Lithuanian Affairs in the summer of 1946 affected the organisation of the fight against the guerilla movement, which was gradually taken over by the professional security officers of the centre.

Entity and relation extractions for threat intelligence knowledge graphs

Advanced persistent threats (APTs) represent a complex challenge in cybersecurity as they infiltrate networks stealthily to conduct espionage, steal data, and maintain a long-term presence. To combat these threats, security professionals increasingly rely on cyber knowledge graphs (CKGs), which provide scalable solutions to analyze and structure vast amounts of cyber threat intelligence (CTI) from diverse sources in real-time, enabling the automation of proactive security measures. Developing CKGs requires extracting entity and their relationships from unstructured CTI reports. However, existing approaches face significant limitations, such as difficulties with the nuances of cybersecurity language, diverse threat terminologies, and high rates of error propagation, resulting in low accuracy and poor generalizability. This paper introduces a novel Threat Intelligence Knowledge Graph (TiKG) pipeline designed to address these challenges. The TiKG framework leverages SecureBERT, a domain-specific transformer-based model optimized for cybersecurity, and integrates it with an attention-based BiLSTM to capture the context and nuances of security texts, reducing error propagation and improving extraction accuracy. Additionally, the pipeline incorporates a domain-specific ontology and inference model to ensure precise relation mapping in relation extraction. Using three large-scale TI open-source datasets (DNRTI, STUCCO, and CYNER) and a curated CTI dataset, extensive evaluations demonstrate the effectiveness of our framework, showing significant improvements over existing methods in detecting and linking cyber threats. These contributions provide a robust platform for security professionals to analyze and predict potential attacks, develop effective defenses, and enhance the strategic capabilities of cybersecurity operations.

Review of Knowledge on the Graphs of Cyber-Attacks by 2024 for IBIMA Publishing Journals

An attack graph is a visual tool used to represent potential attack paths, that a cybercriminal can use to gain access to a computer system or network. It consists of nodes and edges, where Nodes represent resources such as computers, servers, applications or data. Edges represent dependencies between nodes, such as network connections or data flows. Attack graphs are visual tools used to analyse and understand different types of attacks, including cyber-attacks, physical attacks and terrorist attacks. These visualisations help security professionals, analysts and decision makers to identify trends, assess risks and develop defence strategies. Attack graphs are a valuable tool that can help organisations identify, assess and mitigate cyber security risks. They are easy to use and can be customised to suit different needs.

Functional features of well-known means of network shielding

The work briefly reviews the history, types, and capabilities of the main types of firewalls (FW). Firewalls are an important tool for protecting network resources from various information security threats. With the development of technology and the changing nature of attacks, especially those involving artificial intelligence (IoT), firewalls have also evolved, acquiring new functions and capabilities. This work provides a short survey of the main types, and capabilities of firewall technology, providing solutions to issues of comprehensive protection of network equipment and information resources from modern security threats. Different types of firewalls are used depending on the conditions of operation and purpose of the basic information and communication system (ICS), as well as on the place of their (FW) integration into the network or virtual infrastructure of modern information systems. For integrated networks that require a high level of their security, productivity and flexibility, firewalls of the business segment of generation «Next-generation» and «Threat-focused NGFW» are definitely the best choice. Attention was drawn to the fact that mobile firewalls should in every way contribute to the maintenance of resource consensus and eliminate a possible disparity in the performance of networked mobile applications. Adaptability to mobility of current communication systems (Wi-Fi, GSM and others) determines the specificity of security threats for mobile devices and It defines their key feature. This feature is based on permanent readiness for seamless transitions (reconnections) between different networks in conditions of constant energy shortage and limited available computing resources (meaning gadgets). Highlights the main trends, prospects for the development and implementation of different types of firewalls, including the impact of artificial intelligence, machine learning, cloud technologies and the Internet of Things as well as important aspects of their (FW) scope. It is emphasized that the introduction of FW does not replace other security technologies and tools, but effectively expands the existing arsenal of countering new security threats (primarily as an instrument of proactive countermeasures and rapid response to complex network incidents). The article may be useful for students, researchers, and information security professionals who seek to expand their competencies related to the development and operation of modern means of network protection.

CLOUD SECURITY POSTURE MANAGEMENT AUTOMATING RISK IDENTIFICATION AND RESPONSE IN CLOUD INFRASTRUCTURES

Cloud Security Posture Management (CSPM) tools have become essential in addressing the growing security challenges faced by organizations as they migrate to cloud environments. This study explores the effectiveness of CSPM tools in automating the identification and response to security risks within cloud infrastructures, highlighting their role in reducing misconfigurations, improving compliance, and enhancing overall security posture. Through a mixed-method approach, combining a comprehensive literature review, a survey of IT security professionals, and detailed case study analyses, this research provides a robust evaluation of CSPM tools' capabilities and the challenges associated with their implementation. The findings reveal that organizations utilizing CSPM tools experience significant reductions in security incidents and operational inefficiencies, with automation playing a crucial role in enabling real-time threat detection and response. However, the study also identifies critical barriers to CSPM adoption, including integration complexities, cost concerns, and organizational resistance to automated security solutions. These challenges suggest that while CSPM tools offer substantial benefits, their successful deployment requires careful planning, adequate resource allocation, and strategic change management to address both technical and human factors. This study contributes to the existing literature by providing detailed insights into the practical applications and limitations of CSPM tools, offering valuable guidance for organizations seeking to enhance their cloud security strategies through automation.

Improving cyber risk governance through storytelling

This paper addresses the critical challenge of cyber risk governance faced by executives, security committees and boards of directors in the rapidly changing digital landscape. Cyber security complexity, characterised by data deluges and the translational gap between technical jargon and business risk, significantly hinders effective cyber risk messaging and governance. Drawing on five years of research and interviews with chief information security officers (CISOs), the paper highlights the struggle in establishing trust and confidence in governance bodies due to these complexities. It introduces three constructs that aim to simplify cyber security messaging to enhance cyber risk governance: the intelligence to risk (I2R) pyramid, five risk impacts, and resilience and proximity graph. Each construct, illustrated with practical examples, is designed to provide clarity and foster understanding between cyber security professionals and governance bodies, ensuring a cohesive approach to cyber risk management. Readers can expect to gain valuable insights into overcoming the limitations of traditional risk communication tools such as risk registers. By adopting the presented storytelling approach, the paper promises strategies for building trust through transparency and accountability, bridging the communication gap between technical and executive levels, and facilitating informed decision making for improved governance outcomes in the face of cyber security threats.

The Future Perfect of Suspicion and Prediction as a Dispositive of Security Today? The Legacy of Foucault (1977)

This article discusses the current legacy of Michel Foucault in relation to the current political situation. It is articulated in three parts. The first insists on the fact that Michel Foucault has been and still is significant for discussions concerning political sciences and international relations by the way he has discussed them and by his own academic politics. The second part highlights the key role of his attempt to define a dispositif of security in the 1977-78 lecture course ‘Security, Territory, Population’ and the various interpretations given after his death. The third part introduces my own research on the subject and its development. Twenty years ago, I called this dispositif of security surveillance a ban-opticon dispositif. This is only partly relevant since the violence of the effects on individuals has been intensified by a multifocal construction of "suspects" by various transnational guilds of security professionals who systematise profiling and weak correlations as an alternative method of seeking the truth about causalities and facts attributed to an individual. Because of this systematicity of "suspicion first", which jeopardises the principle of innocence, I call this dispositif of security a transnational dispositif of suspicion-prediction, which is organised both as a rearticulation of the modern episteme with suspicion back at its core and as a "legitimate" one, thus allowing a "preventive" violence to be re-enacted in the name of scientific predictions of a future so deadly that it is necessary to act violently now in order to prevent even more violence. This question of inverted temporality, in which the imagined future dominates the present, leads to the belief that the future can already be known under a grammar of the future perfect. Combined with the strategic orientation of right-wing parties to abandon the celebration of the past in order to mobilise the fear of apocalyptic futures, this characteristic of the ‘future-perfect’ explains a series of contemporary developments in security and surveillance, reframing the attachment of the population to a new form of conservatism that captures the imagination of the future, including some contemporary discourses of war. Resisting this attraction to the future-perfect is possible by reinventing hope.

Security Vulnerabilities of Wlan Protocols: A Review

This study reviews a significant number of known attacks with the security methods that are currently available for wireless local area networks (WLANs). After a thorough review of more than a dozen literature on the security issues affecting WLANs, it was found that the architecture of the WLAN itself has a vulnerability that hackers can exploit, especially the wired equivalent privacy (WEP) protocol. WLAN attacks are a combination of human and technology behavior. Human network compromise is still a serious danger to wireless technology, although new encryption technologies are being developed to counteract threats, they are not enough to address the issues at hand because hackers are constantly coming up with inventive ways to breach networks and undermine the standards. Therefore, it is advised to integrate more sophisticated and pervasive multidimensional AI algorithms at different wireless protocol layers as well as through certified ethical hacking experimentation standards in order to improve detection capabilities, identify unauthorized entry through their behaviors, and safeguard wireless connections and data. Also, Cyber security professionals should take note of this report and be proactive in their search for a sophisticated solution to the current issue.