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 >

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 >

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 >

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 >

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 >

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 >

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 >

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 >

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 >

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 >