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REVIEW

Daskshnamoorthy Manivannan^*
Journal of Cyber Security, Vol.7, pp. 505-531, 2025, DOI:10.32604/jcs.2025.072810 - 28 November 2025
Abstract Attribute-Based Encryption (ABE) secures data by linking decryption rights to user attributes rather than user identities, enabling fine-grained access control. While ABE is effective for enforcing access policies, integrating it with Searchable Encryption (SE)—which allows searching encrypted data without decryption—remains a complex challenge. This paper presents a comprehensive survey of ABE schemes that support SE proposed over the past decade. It critically analyzes their strengths, limitations, and access control capabilities. The survey offers insights into the security, efficiency, and practical applicability of these schemes, outlines the current landscape of ABE-integrated SE, and identifies key challenges More >

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40

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Chaonan Xin^*, Keqing Xu
Journal of Cyber Security, Vol.7, pp. 483-503, 2025, DOI:10.32604/jcs.2025.071627 - 28 November 2025
Abstract Intrusion Detection Systems (IDS) have achieved high accuracy on benchmark datasets, yet models often fail to generalize across different network environments. In this paper, we propose Transformer-IDS, a transformer-based network intrusion detection model designed for cross-dataset generalization. The model incorporates a classification token, multi-head self-attention, and embedding layers to learn versatile features, and it introduces a calibration module and an AUC-oriented optimization objective to improve reliability and ranking performance. We evaluate Transformer-IDS on three prominent datasets (NSL-KDD, UNSW-NB15, CIC-IDS2017) in both within-dataset and cross-dataset scenarios. Results demonstrate that while conventional deep IDS models (e.g., CNN-LSTM More >

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129

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50

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Boniface Wambui^1,*, Margaret Mwinji¹, Hellen Nyambura²
Journal of Cyber Security, Vol.7, pp. 463-482, 2025, DOI:10.32604/jcs.2025.069444 - 23 October 2025
Abstract As universities rapidly embrace digital transformation, their growing dependence on interconnected systems for academic, research, and administrative operations has significantly heightened their exposure to sophisticated cyber threats. Traditional defenses such as firewalls and signature-based intrusion detection systems have proven inadequate against evolving attacks like malware, phishing, ransomware, and advanced persistent threats (APTs). This growing complexity necessitates intelligent, adaptive, and anticipatory cybersecurity strategies. Artificial Intelligence (AI) offers a transformative approach by enabling automated threat detection, anomaly identification, and real-time incident response. This study sought to design and evaluate an AI-driven cybersecurity framework specifically for university networks… More >

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480

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349

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Xuezhi Wen^1,2, Eric Danso^1,2,*, Solomon Danso¹
Journal of Cyber Security, Vol.7, pp. 439-462, 2025, DOI:10.32604/jcs.2025.070587 - 17 October 2025
Abstract Cloud-based intrusion detection systems increasingly face sophisticated adversarial attacks such as evasion and poisoning that exploit vulnerabilities in traditional machine learning (ML) models. While deep learning (DL) offers superior detection accuracy for high-dimensional cloud logs, it remains vulnerable to adversarial perturbations and lacks interpretability. Conversely, Hidden Markov Models (HMMs) provide probabilistic reasoning but struggle with raw, sequential cloud data. To bridge this gap, we propose a Deep Learning-Enhanced Ensemble Hidden Markov Model (DL-HMM) framework that synergizes the strengths of Long Short-Term Memory (LSTM) networks and HMMs while incorporating adversarial training and ensemble learning. Our architecture… More >

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1315

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Brian Mweu^1,*, John Ndia²
Journal of Cyber Security, Vol.7, pp. 417-437, 2025, DOI:10.32604/jcs.2025.071765 - 10 October 2025
Abstract Static analysis methods are crucial in developing secure software, as they allow for the early identification of vulnerabilities before the software is executed. This systematic review follows Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to assess static analysis techniques for software security enhancement. We systematically searched IEEE Xplore, Association for Computing Machinery (ACM) Digital Library, SpringerLink, and ScienceDirect for journal articles published between 2017 and 2025. The review examines hybrid analyses and machine learning integration to enhance vulnerability detection accuracy. Static analysis tools enable early fault detection but face persistent challenges. More >

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945

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724

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Victor Otieno Mony^*, Anselemo Peters Ikoha, Roselida O. Maroko
Journal of Cyber Security, Vol.7, pp. 395-415, 2025, DOI:10.32604/jcs.2025.070952 - 30 September 2025
Abstract The widespread adoption of Cloud-Based Services has significantly increased the surface area for cyber threats, particularly targeting authentication mechanisms, which remain among the most vulnerable components of cloud security. This study aimed to address these challenges by developing and evaluating an Intelligent Zero Trust Architecture model tailored to mitigate authentication-related threats in Cloud-Based Services environments. Data was sourced from public repositories, including Kaggle and the National Institute for Standards and Technology MITRE Corporation’s Adversarial Tactics, Techniques, & Common Knowledge (ATT&CK) framework. The study utilized two trust signals: Behavioral targeting system users and Contextual targeting system… More >

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336

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180

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ARTICLE

Asere Gbenga Femi^1,*, Monday Osagie Adenomon¹, Gilbert Imuetinyan Osaze Aimufua¹, Umar Ibrahim²
Journal of Cyber Security, Vol.7, pp. 377-394, 2025, DOI:10.32604/jcs.2025.069185 - 30 September 2025
Abstract Effective data protection frameworks are vital for safeguarding personal information, fostering digital trust, and ensuring alignment with global standards. In Nigeria, the Nigeria Data Protection Regulation (NDPR), administered by the National Information Technology Development Agency (NITDA), constitutes the nation’s primary privacy framework, harmonized with principles of the European Union’s GDPR. This study evaluates NDPR compliance across six strategic sectors; finance, telecommunications, education, health, Small and Medium-sized Enterprises (SMEs), and the public sector using a mixed-methods design. Data from 615 respondents in 30 organizations were collected through surveys, interviews, and document analysis. Findings reveal notable sectoral… More >

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394

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194

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Idarous Saleh Said¹, Gilbert Gilibrays Ocen^1,*, Mwase Ali², Alunyu Andrew Egwar¹
Journal of Cyber Security, Vol.7, pp. 359-375, 2025, DOI:10.32604/jcs.2025.066979 - 25 September 2025
Abstract The growing reliance on digital evidence in judicial proceedings has heightened the need for structured, secure, and legally sound frameworks for its collection, preservation, storage, and presentation. In Zanzibar, however, the integration of digital evidence into the court system remains hindered by the absence of standardized procedures and digital infrastructure, undermining the integrity and admissibility of such evidence. This study addresses these challenges by developing a comprehensive Digital Evidence Lifecycle Management Framework (DELM-C) tailored to the operational and legal context of the Zanzibar High Court. The proposed framework aims to streamline digital evidence handling, enhance… More >

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1396

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442

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Junzhe Cheng^*
Journal of Cyber Security, Vol.7, pp. 343-357, 2025, DOI:10.32604/jcs.2025.067979 - 22 September 2025
Abstract We present a novel Transformer-based network intrusion detection system (IDS) that automatically learns complex feature relationships from raw traffic. Our architecture embeds both categorical (e.g., protocol, flag) and numerical (e.g., packet count, duration) inputs into a unified latent space with positional encodings, and processes them through multi-layer multi-head self-attention blocks. The Transformer’s global attention enables the IDS to capture subtle, long-range correlations in the data (e.g., coordinated multi-step attacks) without manual feature engineering. We complement the model with extensive data augmentation (SMOTE, GANs) to mitigate class imbalance and improve robustness. In evaluation on benchmark datasets… More >

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2856

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2082

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Michael Juma Ayuma^1,*, Shem Mbandu Angolo¹, Philemon Nthenge Kasyoka², Simon Maina Karume³
Journal of Cyber Security, Vol.7, pp. 307-341, 2025, DOI:10.32604/jcs.2025.067360 - 02 September 2025
Abstract Token transmission is a fundamental component in diverse domains, including computer networks, blockchain systems, distributed architectures, financial transactions, secure communications, and identity verification. Ensuring optimal performance during transmission is essential for maintaining the efficiency of data in transit. However, persistent threats from adversarial actors continue to pose significant risks to the integrity, authenticity, and confidentiality of transmitted data. This study presents a comprehensive review of existing research on token transmission techniques, examining the roles of transmission channels, emerging trends, and the associated security and performance implications. A critical analysis is conducted to assess the strengths, More >

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566

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368

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REVIEW

Josh McNett¹, Jackie McNett^2,*
Journal of Cyber Security, Vol.7, pp. 279-306, 2025, DOI:10.32604/jcs.2025.067514 - 25 July 2025
Abstract This review explores the implementation and effectiveness of continuous evaluation programs in managing and mitigating insider threats within organizations. Continuous evaluation programs involve the ongoing assessment of individuals’ suitability for access to sensitive information and resources by monitoring their behavior, access patterns, and other indicators in real-time. The review was conducted using a comprehensive search across various academic and professional databases, including IEEE Xplore, SpringerLink, and Google Scholar and papers were selected from a time span of 2015–2023. The review outlines the importance of defining the scope and objectives of such programs, which should include… More >

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739

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453

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Qiang Xiong^*, Jianlong Zhang, Qianwen Song
Journal of Cyber Security, Vol.7, pp. 255-277, 2025, DOI:10.32604/jcs.2025.065625 - 14 July 2025
Abstract In the domain of information security outsourcing, the multi-stage game-theoretic decision-making process, intertwined with moral hazard and dynamic strategy adjustments, significantly impacts the long-term collaboration between the principal (outsourcing enterprise) and the contractor (Managed Security Service Provider—MSSP). This paper conducts a comprehensive analysis of these aspects within information security outsourcing partnerships. A multi-stage game model incorporating moral hazard is constructed to meticulously examine the strategic behaviors and expected revenue fluctuations of both parties across different cooperation stages. Through in-depth model derivation, the impacts of service fees, cooperation-stage progression, and long-term cooperation on expected revenues are… More >

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543

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391

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Hossana Maghiri Sufficient^*, Abdulazeez Murtala Mohammed, Bashir Danjuma
Journal of Cyber Security, Vol.7, pp. 239-253, 2025, DOI:10.32604/jcs.2025.066312 - 14 July 2025
Abstract The rapid integration of artificial intelligence (AI) into ethical hacking practices has transformed vulnerability discovery and threat mitigation; however, it raises pressing ethical questions regarding responsibility, justice, and privacy. This paper presents a PRISMA-guided systematic review of twelve peer-reviewed studies published between 2015 and March 2024, supplemented by Braun and Clarke’s thematic analysis, to map four core challenges: (1) autonomy and human oversight, (2) algorithmic bias and mitigation strategies, (3) data privacy preservation mechanisms, and (4) limitations of General Data Protection Regulation (GDPR) and the European Union’s AI Act in addressing AI-specific risks, alongside the… More >

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483

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Harri Renney^1,*, Isaac V Chenchiah², Maxim Nethercott¹, Rohini Paligadu¹, James Lang¹
Journal of Cyber Security, Vol.7, pp. 221-238, 2025, DOI:10.32604/jcs.2025.064958 - 14 July 2025
Abstract In recent years, the field of cybersecurity has expanded to encompass a deeper understanding of best practices, user behaviour, and the tactics, motivations, and targets of threat actors. At the same time, there is growing interest in how cyber data analytics can support informed decision-making at senior levels. Despite the broader advancements, the field still lacks a robust scientific foundation for accurately calculating cyber vulnerability risk. Consequently, vulnerabilities in hardware and software systems often remain unaddressed for extended periods, undermining the effectiveness of risk mitigation efforts. This paper seeks to address the gap in vulnerability… More >

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885

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472

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Manzheng Yuan^1,2, Kai Yang^2,*
Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025
Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >

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463

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Issah Zabsonre Alhassan^1,2,*, Gaddafi Abdul-Salaam¹, Michael Asante¹, Yaw Marfo Missah¹, Alimatu Sadia Shirazu¹
Journal of Cyber Security, Vol.7, pp. 165-196, 2025, DOI:10.32604/jcs.2025.063529 - 07 July 2025
Abstract Pseudorandom number generators (PRNGs) are foundational to modern cryptography, yet existing approaches face critical trade-offs between cryptographic security, computational efficiency, and adaptability to emerging threats. Traditional PRNGs (e.g., Mersenne Twister, LCG) remain widely used in low-security applications despite vulnerabilities to predictability attacks, while machine learning (ML)-driven and chaos-based alternatives struggle to balance statistical robustness with practical deployability. This study systematically evaluates traditional, chaos-based, and ML-driven PRNGs to identify design principles for next-generation systems capable of meeting the demands of high-security environment like blockchain and IoT. Using a framework that quantifies cryptographic robustness (via NIST SP… More >

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512

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ARTICLE

Naif Alsharabi^1,*, Akashdeep Bhardwaj^2,*
Journal of Cyber Security, Vol.7, pp. 135-163, 2025, DOI:10.32604/jcs.2025.064019 - 18 June 2025
Abstract In a world driven by unwavering moral principles rooted in ethics, the widespread exploitation of human beings stands universally condemned as abhorrent and intolerable. Traditional methods employed to identify, prevent, and seek justice for human trafficking have demonstrated limited effectiveness, leaving us confronted with harrowing instances of innocent children robbed of their childhood, women enduring unspeakable humiliation and sexual exploitation, and men trapped in servitude by unscrupulous oppressors on foreign shores. This paper focuses on human trafficking and introduces intelligent technologies including graph database solutions for deciphering unstructured relationships and entity nodes, enabling the comprehensive More >

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671

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429

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Ananya Jha¹, Amaresh Jha^2,*
Journal of Cyber Security, Vol.7, pp. 109-134, 2025, DOI:10.32604/jcs.2025.064429 - 30 May 2025
Abstract This paper explores the methodologies employed in the study of mobile and social media phishing, aiming to enhance the understanding of these evolving threats and develop robust countermeasures. By synthesizing existing research, we identify key approaches, including surveys, controlled experiments, data mining, and machine learning, to gather and analyze data on phishing tactics. These methods enable us to uncover patterns in attacker behavior, pinpoint vulnerabilities in mobile and social platforms, and evaluate the effectiveness of current detection and prevention strategies. Our findings highlight the growing sophistication of phishing techniques, such as social engineering and deceptive More >

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956

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617

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Kagiso Komane^1,*, Lucas Khoza², Fani Radebe¹
Journal of Cyber Security, Vol.7, pp. 79-108, 2025, DOI:10.32604/jcs.2025.059712 - 20 May 2025
Abstract Financial support, government support, cyber hygiene, and ongoing education and training as well as parental guidance and supervision are all essential components of cybersecurity awareness (CSA) identified in this study among the youth. It’s critical to realize that adequate funding is needed to effectively increase CSA, particularly among South African youth. Previous studies have demonstrated several ways to address inadequate CSA by utilizing various cybersecurity frameworks, ideas, and models. To increase CSA, this literature review seeks to emphasize the significance of integrating cybersecurity education throughout the entire school curriculum. This paper identified ethical issues, protection… More >

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771

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David Conway, Paolina Centonze^*
Journal of Cyber Security, Vol.7, pp. 71-78, 2025, DOI:10.32604/jcs.2025.063711 - 14 May 2025
Abstract Ransomware is a variant of malicious software that aims to encrypt data or whole systems to lock out the intended users. The attackers then demand a ransom for the decryption key to allow the intended users access to their data or system again. Ransomware attacks have the potential to be used against industries like healthcare and finance, as well as against the public sector, have threatened and forced the operations of key infrastructure used by millions to cease, and extorted millions and millions of dollars from victims. Automated methods have been designed and implemented to More >

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961

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614

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Xuezhi Wen¹, Eric Danso^2,*, Solomon Danso²
Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606 - 08 May 2025
Abstract Deep learning models have achieved remarkable success in healthcare, finance, and autonomous systems, yet their security vulnerabilities to adversarial attacks remain a critical challenge. This paper presents a novel dual-phase defense framework that combines progressive adversarial training with dynamic runtime protection to address evolving threats. Our approach introduces three key innovations: multi-stage adversarial training with TRADES (Tradeoff-inspired Adversarial Defense via Surrogate-loss minimization) loss that progressively scales perturbation strength, maintaining 85.10% clean accuracy on CIFAR-10 (Canadian Institute for Advanced Research 10-class dataset) while improving robustness; a hybrid runtime defense integrating feature manipulation, statistical anomaly detection, and… More >

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3172

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Arkan Kh Shakr Sabonchi^*
Journal of Cyber Security, Vol.7, pp. 21-43, 2025, DOI:10.32604/jcs.2025.059645 - 18 February 2025
Abstract With the internet and modern mobile technologies, health-related information is readily available, and thus, the security aspect of health information is at great risk. Confidentiality and protection of medical information regarding patients are of prime concern in the context of sharing such data with different healthcare providers. On one hand, Electronic Health Record Systems (EHRS) and online sites have proved to be hassle-free ways of exchanging medical information between health professionals. On the other hand, data security issues remain a concern. The proposed paper presents an improvement in the security mechanism of EHRS by utilizing… More >

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1308

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984

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David Njuguna^*, John Ndia
Journal of Cyber Security, Vol.7, pp. 1-20, 2025, DOI:10.32604/jcs.2025.059398 - 18 February 2025
Abstract A quick response code is a barcode that allows users to instantly access information via a digital device. Quick response codes store data as pixels in a square-shaped grid. QR codes are prone to cyber-attacks. This assault exploits human vulnerabilities, as users can scarcely discern what is concealed in the quick response code prior to usage. The aim of the study was to investigate Quick Response code attack types and the detection techniques. To achieve the objective, 50 relevant studies published between the year 2010 and 2024 were identified. The articles were obtained from the… More >

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