Open Access

ARTICLE

The Impact of SWMF Features on the Performance of Random Forest, LSTM and Neural Network Classifiers for Detecting Trojans

Fatemeh Ahmadi Abkenari^*, Melika Zandi, Shanmugapriya Gopalakrishnan

Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026
Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

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A Systematic Review of Frameworks for the Detection and Prevention of Card-Not-Present (CNP) Fraud

Kwabena Owusu-Mensah^*, Edward Danso Ansong , Kofi Sarpong Adu-Manu, Winfred Yaokumah

Journal of Cyber Security, Vol.8, pp. 33-92, 2026, DOI:10.32604/jcs.2026.074265 - 20 January 2026
Abstract The rapid growth of digital payment systems and remote financial services has led to a significant increase in Card-Not-Present (CNP) fraud, which is now the primary source of card-related losses worldwide. Traditional rule-based fraud detection methods are becoming insufficient due to several challenges, including data imbalance, concept drift, privacy concerns, and limited interpretability. In response to these issues, a systematic review of twenty-four CNP fraud detection frameworks developed between 2014 and 2025 was conducted. This review aimed to identify the technologies, strategies, and design considerations necessary for adaptive solutions that align with evolving regulatory standards.… More >

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60

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ARTICLE

A Decentralized Identity Framework for Secure Federated Learning in Healthcare

Samuel Acheme^*, Glory Nosawaru Edegbe

Journal of Cyber Security, Vol.8, pp. 1-31, 2026, DOI:10.32604/jcs.2026.073923 - 07 January 2026
Abstract Federated learning (FL) enables collaborative model training across decentralized datasets, thus maintaining the privacy of training data. However, FL remains vulnerable to malicious actors, posing significant risks in privacy-sensitive domains like healthcare. Previous machine learning trust frameworks, while promising, often rely on resource-intensive blockchain ledgers, introducing computational overhead and metadata leakage risks. To address these limitations, this study presents a novel Decentralized Identity (DID) framework for mutual authentication that establishes verifiable trust among participants in FL without dependence on centralized authorities or high-cost blockchain ledgers. The proposed system leverages Decentralized Identifiers (DIDs) and Verifiable Credentials… More >

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186

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ARTICLE

Improving Security-Sensitive Deep Learning Models through Adversarial Training and Hybrid Defense Mechanisms

Xuezhi Wen¹, Eric Danso^2,*, Solomon Danso²

Journal of Cyber Security, Vol.7, pp. 45-69, 2025, DOI:10.32604/jcs.2025.063606
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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3305

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ARTICLE

Deep Learning-Driven Intrusion Detection and Defense Mechanisms: A Novel Approach to Mitigating Cyber Attacks

Junzhe Cheng^*

Journal of Cyber Security, Vol.7, pp. 343-357, 2025, DOI:10.32604/jcs.2025.067979
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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2230

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REVIEW

Enhancing Cyber Security through Artificial Intelligence and Machine Learning: A Literature Review

Carlos Merlano^*

Journal of Cyber Security, Vol.6, pp. 89-116, 2024, DOI:10.32604/jcs.2024.056164
Abstract The constantly increasing degree and frequency of cyber threats require the emergence of flexible and intelligent approaches to systems’ protection. Despite the calls for the use of artificial intelligence (AI) and machine learning (ML) in strengthening cyber security, there needs to be more literature on an integrated view of the application areas, open issues or trends in AI and ML for cyber security. Based on 90 studies, in the following literature review, the author categorizes and systematically analyzes the current research field to fill this gap. The review evidences that, in contrast to rigid rule-based… More >

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1269

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ARTICLE

A Comparative Performance Analysis of Machine Learning Models for Intrusion Detection Classification

Adil Hussain¹, Amna Khatoon^2,*, Ayesha Aslam², Tariq¹, Muhammad Asif Khosa¹

Journal of Cyber Security, Vol.6, pp. 1-23, 2024, DOI:10.32604/jcs.2023.046915
Abstract The importance of cybersecurity in contemporary society cannot be inflated, given the substantial impact of networks on various aspects of daily life. Traditional cybersecurity measures, such as anti-virus software and firewalls, safeguard networks against potential threats. In network security, using Intrusion Detection Systems (IDSs) is vital for effectively monitoring the various software and hardware components inside a given network. However, they may encounter difficulties when it comes to detecting solitary attacks. Machine Learning (ML) models are implemented in intrusion detection widely because of the high accuracy. The present work aims to assess the performance of More >

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1725

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ARTICLE

Adversarial-Resistant Cloud Security Using Deep Learning-Enhanced Ensemble Hidden Markov Models

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
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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1433

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ARTICLE

Enhancing Private Cloud Based Intrusion Prevention and Detection System: An Unsupervised Machine Learning Approach

Theophile Fozin Fonzin^1,2, Halilou Claude Bobo Hamadjida², Aurelle Tchagna Kouanou^2,3,*, Valery Monthe⁴, Anicet Brice Mezatio⁵, Michael Sone Ekonde⁶

Journal of Cyber Security, Vol.6, pp. 155-177, 2024, DOI:10.32604/jcs.2024.059265
Abstract Cloud computing is a transformational paradigm involving the delivery of applications and services over the Internet, using access mechanisms through microprocessors, smartphones, etc. Latency time to prevent and detect modern and complex threats remains one of the major challenges. It is then necessary to think about an intrusion prevention system (IPS) design, making it possible to effectively meet the requirements of a cloud computing environment. From this analysis, the central question of the present study is to minimize the latency time for efficient threat prevention and detection in the cloud. To design this IPS design… More >

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5166

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ARTICLE

Sentence Level Analysis Model for Phishing Detection Using KNN

Lindah Sawe^*, Joyce Gikandi, John Kamau, David Njuguna

Journal of Cyber Security, Vol.6, pp. 25-39, 2024, DOI:10.32604/jcs.2023.045859
Abstract Phishing emails have experienced a rapid surge in cyber threats globally, especially following the emergence of the COVID-19 pandemic. This form of attack has led to substantial financial losses for numerous organizations. Although various models have been constructed to differentiate legitimate emails from phishing attempts, attackers continuously employ novel strategies to manipulate their targets into falling victim to their schemes. This form of attack has led to substantial financial losses for numerous organizations. While efforts are ongoing to create phishing detection models, their current level of accuracy and speed in identifying phishing emails is less… More >

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ARTICLE

Performance Evaluation of Machine Learning Algorithms in Reduced Dimensional Spaces

Kaveh Heidary^1,*, Venkata Atluri¹, John Bland²

Journal of Cyber Security, Vol.6, pp. 69-87, 2024, DOI:10.32604/jcs.2024.051196
Abstract This paper investigates the impact of reducing feature-vector dimensionality on the performance of machine learning (ML) models. Dimensionality reduction and feature selection techniques can improve computational efficiency, accuracy, robustness, transparency, and interpretability of ML models. In high-dimensional data, where features outnumber training instances, redundant or irrelevant features introduce noise, hindering model generalization and accuracy. This study explores the effects of dimensionality reduction methods on binary classifier performance using network traffic data for cybersecurity applications. The paper examines how dimensionality reduction techniques influence classifier operation and performance across diverse performance metrics for seven ML models. Four… More >

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2260

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ARTICLE

A Conceptual Framework for Cybersecurity Awareness

Kagiso Komane^1,*, Lucas Khoza², Fani Radebe¹

Journal of Cyber Security, Vol.7, pp. 79-108, 2025, DOI:10.32604/jcs.2025.059712
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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884

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REVIEW

Quick Response Code Security Attacks and Countermeasures: A Systematic Literature Review

David Njuguna^*, John Ndia

Journal of Cyber Security, Vol.7, pp. 1-20, 2025, DOI:10.32604/jcs.2025.059398
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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