Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Rayed Alakhtar³, Ahmad J. Tayeb³, Badraddin Alturki³, Huda Alsobhi⁴, Rayan A. Alsemmeari³

CMC-Computers, Materials & Continua, Vol.88, No.3, 2026, DOI:10.32604/cmc.2026.083321 - 23 July 2026

Abstract The rapid growth of the Internet of Things (IoT) devices has increased the attack area of modern networks, which makes effective intrusion detection systems (IDSs) essential to detect attacks that target IoT infrastructures. Federated learning is a promising approach for collaborative model training in the absence of centralized raw data. Conventional federated approaches rely on fixed client participation and static training configurations, which ensure symmetric treatment of clients despite heterogeneous local data distributions. This can limit convergence and degrade detection performance in non-IID conditions. This paper proposes an Adaptive Action-Based Federated Learning (AA-FL) framework for… More >

Munthir Qasaimeh¹, Mostafa Ali¹, Qasem Abu Al-Haija^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.147, No.2, 2026, DOI:10.32604/cmes.2026.080864 - 27 May 2026

Abstract Vision Transformers (ViTs) have recently achieved high performance in retinal Optical Coherence Tomography (OCT) classification studies. However, ViT models continue to face significant challenges, including high computational cost, vulnerability to adversarial attacks, and pronounced sensitivity to preprocessing techniques. This study introduces GreenShield, a unified framework designed to produce an efficient and robust ViT model, referred to as GreenShield-ViT, which outperforms existing lightweight ViT variants in terms of adversarial robustness for retinal OCT classification. The framework integrates a gradient-based block-importance pruning strategy to compress the ViT/B-16 architecture, and adversarial training with proper ImageNet normalization and anti-saturation… More >

Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026

Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

Abdulaziz A. Alsulami¹, Qasem Abu Al-Haija^2,*, Badraddin Alturki³, Ayman Yafoz¹, Ali Alqahtani⁴, Raed Alsini¹, Sami Saeed Binyamin⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1117-1140, 2025, DOI:10.32604/cmes.2025.070745 - 30 October 2025

Abstract QR codes are widely used in applications such as information sharing, advertising, and digital payments. However, their growing adoption has made them attractive targets for malicious activities, including malware distribution and phishing attacks. Traditional detection approaches rely on URL analysis or image-based feature extraction, which may introduce significant computational overhead and limit real-time applicability, and their performance often depends on the quality of extracted features. Previous studies in malicious detection do not fully focus on QR code security when combining convolutional neural networks (CNNs) with recurrent neural networks (RNNs). This research proposes a deep learning… More >

Mohammad Al-Omari¹, Qasem Abu Al-Haija^2,*

Computer Systems Science and Engineering, Vol.48, No.6, pp. 1537-1555, 2024, DOI:10.32604/csse.2024.056257 - 22 November 2024

Abstract More businesses are deploying powerful Intrusion Detection Systems (IDS) to secure their data and physical assets. Improved cyber-attack detection and prevention in these systems requires machine learning (ML) approaches. This paper examines a cyber-attack prediction system combining feature selection (FS) and ML. Our technique’s foundation was based on Correlation Analysis (CA), Mutual Information (MI), and recursive feature reduction with cross-validation. To optimize the IDS performance, the security features must be carefully selected from multiple-dimensional datasets, and our hybrid FS technique must be extended to validate our methodology using the improved UNSW-NB 15 and TON_IoT datasets. More >

Qasem Abu Al-Haija^1,*, Sara Othman Al-Salameen²

Computer Systems Science and Engineering, Vol.48, No.4, pp. 897-914, 2024, DOI:10.32604/csse.2024.050846 - 17 July 2024

Abstract The paper discusses the importance of biometric verification systems in mobile environments and highlights the challenges and strategies used to overcome them in order to ensure the security of mobile devices. Emphasis is placed on evaluating the impact of illumination on the performance of biometric verification techniques and how to address this challenge using image processing techniques. The importance of accurate and reliable data collection to ensure the accuracy of verification processes is also discussed. The paper also highlights the importance of improving biometric verification techniques and directing research toward developing models aimed at reducing More >