Cybersecurity Threats Research Articles

Digital Transformation in Higher Education Obstacle Assessment and Development of Strategies against Cybersecurity Threats: The Case of Moroccan Universities

Digital Transformation(DT) in higher education has become essential in improving both educational delivery and operational efficiency. However, this transition also exposes institutions to increasing cybersecurity threats, often associated with various barriers reported in the literature. Although these barriers have been widely studied, no research has yet systematically prioritized them in the academic context. This study, conducted within the framework of DT in Morocco, addresses this gap by classifying and prioritizing these barriers to better understand how they contribute to the spread of cybersecurity threats. Using methodologies such as the Analytic Hierarchy Process (AHP) and the Analytic Network Process (ANP), we not only prioritized the major barriers but also developed specific strategies to counter the resulting threats, revealing significant variations in the prioritization of cybersecurity strategies. These differences arise from the complex interactions between the barriers identified by the ANP, highlighting the importance of considering interdependencies when developing effective cybersecurity strategies.

Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.

Advancing Cybersecurity with Honeypots and Deception Strategies

Cybersecurity threats are becoming more intricate, requiring preemptive actions to safeguard digital assets. This paper examines the function of honeypots as critical instruments for threat detection, analysis, and mitigation. A novel methodology for comparative analysis of honeypots is presented, offering a systematic framework to assess their efficacy. Seven honeypot solutions, namely Dionaea, Cowrie, Honeyd, Kippo, Amun, Glastopf, and Thug, are analyzed, encompassing various categories, including SSH and HTTP honeypots. The solutions are assessed via simulated network attacks and comparative analyses based on established criteria, including detection range, reliability, scalability, and data integrity. Dionaea and Cowrie exhibited remarkable versatility and precision, whereas Honeyd revealed scalability benefits despite encountering data quality issues. The research emphasizes the smooth incorporation of honeypots with current security protocols, including firewalls and incident response strategies, while offering comprehensive insights into attackers’ tactics, techniques, and procedures (TTPs). Emerging trends are examined, such as incorporating machine learning for adaptive detection and creating cloud-based honeypots. Recommendations for optimizing honeypot deployment include strategic placement, comprehensive monitoring, and ongoing updates. This research provides a detailed framework for selecting and implementing honeypots customized to organizational requirements.

Designing a Data Governance Framework for Cybersecurity Risk Reporting: A Model for Business Intelligence Teams

In the rapidly evolving digital landscape, the integration of robust data governance practices is crucial for enhancing cybersecurity risk reporting within organizations. This abstract presents a model for designing a data governance framework tailored specifically for Business Intelligence (BI) teams. The proposed framework emphasizes the intersection of data governance and cybersecurity, ensuring that data management practices support comprehensive risk reporting and decision-making processes. Key elements of the framework include data quality management, data access controls, and data lineage tracking, which collectively contribute to more accurate and timely cybersecurity risk assessments. By embedding these elements into the governance structure, BI teams can better manage and mitigate data-related risks, ensuring that cybersecurity threats are identified and addressed proactively. The model also incorporates advanced analytics and machine learning techniques to automate the detection of potential vulnerabilities, thereby enhancing the efficiency and effectiveness of risk reporting. Furthermore, the framework advocates for a collaborative approach, involving stakeholders from IT, security, compliance, and business units to ensure that data governance policies align with the organization's overall cybersecurity strategy. This interdisciplinary collaboration is essential for fostering a culture of cybersecurity awareness and accountability across the enterprise. In addition to technical considerations, the framework addresses the need for clear governance policies and procedures, regular audits, and continuous monitoring to maintain data integrity and compliance with regulatory requirements. The model is designed to be adaptable, allowing organizations to customize their data governance practices based on their specific industry, regulatory environment, and risk profile. The implementation of this data governance framework is expected to significantly improve the accuracy and reliability of cybersecurity risk reporting, providing BI teams with the tools and insights necessary to support informed decision-making and safeguard organizational assets against emerging threats.

Research Paper on Cybersecurity and Insider Threat Detection: The Role of User Behavior Analytics (UBA) in Modern Defense Strategies

Research Paper on Cybersecurity and Insider Threat Detection: The Role of User Behavior Analytics (UBA) in Modern Defense Strategies

Real-Time Cyber Security Protection Tool

This report aims to examine the evolving cybersecurity threats, including DeepFakes, phishing, social engineering, and malware, and analyze detection mechanisms to counter these threats. DeepFakes, generated using advanced AI techniques, pose risks such as identity theft and disinformation, with detection models like CNNs and RNNs showing promise, albeit with reduced effectiveness against high-quality manipulations. Tools like Face Forensics++ are instrumental for training such models. Phishing, which employs deceptive techniques to steal sensitive information, is addressed through URL-based detection systems and datasets such as Phish-Tank. Social engineering, leveraging tactics like pretexting and baiting, highlights the importance of NLP models for detecting manipulation in communications. Malware, encompassing threats like ransomware and spyware, continues to challenge cybersecurity efforts, with machine learning and deep learning approaches proving effective for detection. Tools like Virus Total and Cuckoo Sandbox enhance detection through multi-engine scanning and dynamic analysis. While detection technologies show significant potential, challenges such as real-time threat identification and user awareness underscore the need for integrated, adaptive cybersecurity solutions

Cybersecurity threats of remote autonomous ships while approaching ports

Cybersecurity threats of remote autonomous ships while approaching ports

Benchmarking Machine Learning Techniques for Phishing Detection and Secure URL Classification

Phishing is still one of the biggest threats in cybersecurity. It is the exploitation of users through the use of deceptive URLs. In this study, the outcomes of the Random Forest, Support Vector Machines, and Decision Tree models are analysed on a dataset containing more than 640,000 URLs. The results showed that Random Forest recorded the highest accuracy of 87.85% on the Aalto dataset and 86.86% on the Kaggle dataset. These perspectives provide a more fact-based approach towards developing more effective and practical anti-phishing systems.

AI-augmented cyber security threat intelligence – enhancing situational awareness

AI-augmented cyber security threat intelligence – enhancing situational awareness

EDUKASI KEAMANAN SIBER DI KOMUNITAS YOUNG OZER INDONESIA SEBAGAI UPAYA MENGURANGI RISIKO TINDAK KEJAHATAN SIBER

The large number of cases of cyber attacks encourages the need for more substantial efforts to understand and overcome them. For this reason, education is needed to increase awareness of cybersecurity threats and reduce the risk of cybercrime. This community service activity is carried out in the Young Ozzer Indonesia community through education and discussions regarding Cyber Crime and cyber security, including email, smartphones and social media. The results of the community service activities are increased knowledge, understanding and awareness of participants regarding cyber security. The increasing knowledge is measured from the post-test results, which show an increase in the scores obtained by participants after the education was carried out; namely, 10 out of 11 (91%) of the participants got a score of 60 or more, and only 1 participant got a score of 50.

The Critical Role of Two-Factor Authentication (2FA) in Mitigating Ransomware and Securing Backup, Recovery, and Storage Systems

In today’s digital landscape, ransomware attacks have become one of the most significant cybersecurity threats. Securing backup, recovery, and storage systems is paramount to ensuring business continuity. Two-Factor Authentication (2FA) has proven to be a key strategy in defending against ransomware attacks by adding an extra layer of security. By requiring two forms of authentication, 2FA reduces the risk of unauthorized access to critical systems and data. This paper explores how 2FA enhances the security of backup systems, recovery processes, and storage devices, with a particular focus on its role in mitigating ransomware risks. It further discusses its impact on compliance with data protection regulations and operational efficiency.

Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System

To ensure security and stable quality, deeper cybersecurity evaluations are essential for the development of safety features and functionalities in vehicles. Among these, the Autonomous emergency braking (AEB) system is the most relevant. This research presents a comprehensive Threat analysis and risk assessment (TARA) of the AEB system, emphasizing the identification, validation, and mitigation of major cybersecurity threats and risks. We systematically examine potential attack vectors by utilizing the STRIDE threat model. This approach enables a detailed analysis of each security threat associated with AEB systems, providing insights into how malicious actors could use the attack paths. The assessment aligns with ISO/SAE 21434, which offers a robust framework for risk management in automotive cybersecurity and IT security, ensuring a thorough evaluation of a system’s architecture. By assessing the AEB system’s architecture against these standards, we identify key components and communication pathways that may be particularly prone to cyberattacks. The results of this analysis highlight critical flaws within the AEB framework and propose corrective measures to enhance cybersecurity resilience. This article provides a structured methodology for assessing and mitigating automotive cybersecurity risks in compliance with industry standards, aiming to facilitate the safe implementation of AEB technology and ultimately improve overall vehicle security and safety.

Challenges Faced by the Judiciary in Implementing Cybersecurity Laws in Pakistan

Due to the inherent growth of technological elements, today’s world of governance, commerce, and societies has been revolutionized, opening new vistas of opportunity as well as new horizons of cyber security threats. Fraud such as hacking, identity theft, and ransom ware are important threats that are knowledgeable about to personal and business associations and the government. In Pakistan, law enforcement agencies and the judiciary are overwhelmed with inadequate resources lack of training, and technical knowledge. However, the institutions operating in the country still face legislative gaps and enforcement challenges even after the passing of the Prevention of Electronic Crimes Act (PECA) 2016 means. The paper deals with Pakistan's situation concerning cyber security enhancement judicial practices and legislation and compares it to the United States’ strategic enhancement in the security domain. There are some leading projects like the National Judicial Automation Program and e-Courts, but still, it seems there are a lot more systems problems and no significant digital forensics available in the judiciary of Pakistan. The comparative analysis indicates that Pakistan needs to reform its laws, train its judges specialized in cybercrime and look for international cooperation when it comes to fighting transnational criminals engaged in cybercrimes. The importance of increasing the level of protection in cyberspace is highlighted by raising public awareness or engaging private structures. This research stresses the need to mitigate cyber security threats through legal, organizational, and social change. From best practices and international collaboration so that Pakistan can better address the issue, increase the capacity of its judicial system in combating cyber threats, and protect its critical IT assets. The actual findings of the research include ways to improve the efficiency of Courts, protect digital rights, and strengthen the governance of cyber security in the country.

FPE–Transformer: A Feature Positional Encoding-Based Transformer Model for Attack Detection

The increase in cybersecurity threats has made attack detection systems critically important. Traditional deep learning methods often require large amounts of data and struggle to understand relationships between features effectively. With their self-attention mechanism, Transformers excel in modeling complex relationships and long-term dependencies. They are also adaptable to various data types and sources, making them advantageous in large-scale attack detection scenarios. This paper introduces the FPE–Transformer framework, leveraging the strengths of the Transformer architecture. FPE–Transformer incorporates an innovative feature positional encoding mechanism that encodes the positional information of each feature separately, enabling a deeper understanding of feature relationships and more precise attack detection. Additionally, the model includes a ClassificationHead for enhanced accuracy and complex pattern recognition. The framework’s performance was validated using the NSL-KDD and CIC-IDS2017 datasets, demonstrating its superiority over traditional methods in detecting diverse attack types and improving overall performance. This study highlights FPE–Transformer’s innovative approach and ability to address key limitations of traditional deep learning methods, establishing it as a robust solution for modern attack detection challenges.

The Intersection of IoT, Marketing, and Cybersecurity: Advantages and Threats for Business Strategy

This paper examines how IoT, marketing, and cybersecurity affect current corporate strategy, including its benefits and drawbacks. The research examines how data-driven insights from IoT improve marketing tactics and the cybersecurity risks of linked devices. The research synthesizes secondary data to explore the dynamic interaction between IoT, marketing, and cybersecurity. The key results show that IoT helps organizations create targeted marketing campaigns using real-time data, but it also increases the attack surface, posing cybersecurity threats. Many IoT devices lack security and data privacy, which threatens corporate operations. The report also stresses integrating strong security measures into design, using adaptive security frameworks, and collaborating between marketing and IT departments. Policy implications include enterprises implementing more apparent cybersecurity principles and educating employees, while legislators should regulate data privacy, secure gadget manufacture, and cross-sector cooperation. This report emphasizes balancing innovation and security to maximize IoT-driven marketing tactics while minimizing cybersecurity concerns.

Collaborative online international learning as a third space to improve students’ awareness of cybersecurity

Abstract This article critically evaluates the effects of the ACU Virtual Mobility (VM) Project Digital Information Security Course (DISC) programme on improving students’ awareness of cyber security skills, pre and post the educational intervention. Our selected VM tool is Collaborative Online International Learning (COIL). Building on a conceptual framework of COIL as a Third Space for teaching students from different countries, we show that COIL has a positive effect on glocalising cybersecurity. This enabled students to share their local experiences but work towards global solutions, since cybersecurity threats are global concerns. A cross-sectional survey design was used, with data collected using online questionnaires. The research was conducted at the Durban University of Technology (DUT), South Africa and at Liverpool John Moores University (LJMU), United Kingdom. Census sampling identified programme participants from DUT and LJMU, registered at the respective universities in 2024. Data were analysed statistically using descriptive statistics, Chi-square test of independence, One sample t-test and independent samples t-test. Results indicated that students reported cyber security as a high priority. This study confirms that students who are aware of cyber bullying have a greater ability to ensure their cyber security. An interesting finding was the difference between DUT and LJMU students on collaboration skills (M = 4.84, t=(48.301) 2.478, p = .017). The major contribution is the use of COIL as an educational intervention to increase students’ cyber security awareness and enrich their learning experiences. Educators should consider adopting COIL as a pedagogical tool to educate students on cybersecurity and AI. This project has policy impact on social mobility and educational policies of the UK. We provide evidence to the ongoing dialogue in South Africa regarding student mobility and a stronger emphasis on short-term virtual mobility exchanges. This study focused on students who participated in the ACU virtual mobility project, DISC; therefore, the results may not be generalised to other projects of a similar nature. This study does not consider the long-term effects of this intervention on increasing cyber security awareness because it was of a cross-sectional design.

Low-impact, near real-time risk assessment for legacy IT infrastructures

In an era where cybersecurity threats are evolving at an unprecedented pace, this paper introduces a methodology for near real-time risk assessment of high-profile, high security infrastructures, where data security and operational continuity inherently limits observability. Our approach addresses the challenges of this limited observability and minimized disruption, offering a new perspective on processing and evaluating cybersecurity knowledge. We present an innovative method that leverages attack graphs and attacker behavior analysis to assess risks and vulnerabilities. Our research includes the development of an automated risk assessment mechanism, graphical security modeling, and a Markov chain-based model for attacker behavior. Our methodology utilizes a blend of direct and indirect event sources, incorporating an attacker behavioral model based on a random walk method akin to Google’s PageRank. The proof-of-concept solution calculates potential risk according to the actual threat landscape, providing a more accurate and timely assessment.

Психологические факторы кибербезопасности и доверие к ложным новостям в интернет-коммуникации: обзор современных зарубежных исследований

<p style="text-align: justify;">The paper presents a systematic review of contemporary foreign research on psychological factors of cybersecurity and trust in fake news. It contains an analysis of theoretical developments and empirical studies of the psychological aspects of cybersecurity within the framework of the concepts of personality psychology, behavioral psychology and social psychology. Despite the fact that general psychological patterns and theoretical models of these branches of psychology are used in the study of cybersecurity, the available results of research in this area still do not form a holistic picture of the psychological factors of cybersecurity behavior. At the same time, the factors of personal characteristics and behavior of cybercriminals seem to be better studied and more understandable than the factors of compliance and violation of the rules of cybersecurity behavior by “ordinary” users. The article then presents a review of empirical studies on the reasons why Internet users trust fake news and how to overcome it. General awareness of the problem of the spread of fake news is shown to offer little help in reducing trust in fake news, and the use of ineffective strategies for recognizing fake news often has the opposite effect. A more sophisticated strategy based on knowledge of the specific techniques by which fake news is created, on the contrary, can more effectively reduce the risk of trust in fake news. The author comes to the conclusion that measures promoting cybersecurity behavior of Internet users are advisable if aimed not at stimulating vigilance, but at increasing confidence in the picture of the world, in which the phenomena of cybersecurity threats and fake news are built in as a familiar and understandable component.</p>

LEVERAGING AI AND MACHINE LEARNING FOR PREDICTING, DETECTING, AND MITIGATING CYBERSECURITY THREATS: A COMPARATIVE STUDY OF ADVANCED MODELS

This study investigates the use of artificial intelligence (AI) and machine learning (ML) models to predict, detect, and mitigate cybersecurity threats, including zero-day attacks, ransomware, and insider threats. Using a comprehensive dataset of network logs and attack signatures, we evaluated models such as Logistic Regression, Random Forest, XGBoost, CNN, and LSTMOur results demonstrate that deep learning models, particularly CNN (97.3% AUC-ROC) and LSTM (96.8% AUC-ROC), significantly outperform traditional methods, excelling in real-time threat detection and minimizing false positives. This study highlights the practical applicability of AI and ML in enhancing cybersecurity frameworks, paving the way for more efficient and scalable solutions against evolving threats.

Strengthening Compliance with Data Privacy Regulations in U.S. Healthcare Cybersecurity

This study evaluates the state of data privacy and cybersecurity compliance within the U.S. healthcare sector, leveraging data from the U.S. Department of Health & Human Services Breach Portal, Verizon Data Breach Investigations Report, and the Health IT Dashboard. A quantitative methodology comprising descriptive statistical analysis, K-means clustering, and multivariate regression was employed to examine healthcare data breaches, categorize cybersecurity threats, and identify compliance challenges. Findings revealed a persistent increase in breaches, with hacking/IT incidents comprising over 80% of breaches in 2020 and a peak of 135 incidents in 2021. Budget allocation emerged as the most significant predictor of compliance (p = 0.0178), affirming resource constraints. Malware and ransomware were identified as dominant threats, while insider threats emerged as high-impact vulnerabilities. The study recommends increasing cybersecurity budgets, implementing continuous staff training, harmonizing regulations, and adopting Cybersecurity Maturity Models to systematically enhance security postures. The study provides critical insights into the challenges faced by healthcare organizations in achieving compliance with evolving data privacy regulations such as HIPAA and HITECH. The findings highlight the economic and operational implications of non-compliance, including financial penalties, reputational harm, and patient trust erosion. The study further affirms the importance of strategic investments in advanced cybersecurity tools, policy harmonization, and employee education. Hence, policymakers and healthcare administrators can utilize these insights to foster a robust culture of compliance, ensuring the protection of sensitive patient information and the resilience of healthcare operations against cyber threats. The study suggests that future research explores integrating artificial intelligence, zero-trust architectures, and adaptive risk management frameworks to further enhance cybersecurity strategies and regulatory compliance.