Muhammad Asim^1,*, Abdelhamied A. Ateya¹, Mudasir Ahmad Wani^1,2, Gauhar Ali¹, Mohammed ElAffendi¹, Ahmed A. Abd El-Latif¹, Reshma Siyal³

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.070684 - 12 January 2026

Abstract The growing developments in 5G and 6G wireless communications have revolutionized communications technologies, providing faster speeds with reduced latency and improved connectivity to users. However, it raises significant security challenges, including impersonation threats, data manipulation, distributed denial of service (DDoS) attacks, and privacy breaches. Traditional security measures are inadequate due to the decentralized and dynamic nature of next-generation networks. This survey provides a comprehensive review of how Federated Learning (FL), Blockchain, and Digital Twin (DT) technologies can collectively enhance the security of 5G and 6G systems. Blockchain offers decentralized, immutable, and transparent mechanisms for securing More >

Ahsan Habib Siam¹, Md. Ehsanul Haque¹, Fahmid Al Farid², Anindita Sutradhar³, Jia Uddin^4,*, Sarina Mansor^2,*

CMC-Computers, Materials & Continua, Vol.86, No.3, 2026, DOI:10.32604/cmc.2025.069718 - 12 January 2026

Abstract As healthcare systems increasingly embrace digitalization, effective management of electronic health records (EHRs) has emerged as a critical priority, particularly in inpatient settings where data sensitivity and real-time access are paramount. Traditional EHR systems face significant challenges, including unauthorized access, data breaches, and inefficiencies in tracking follow-up appointments, which heighten the risk of misdiagnosis and medication errors. To address these issues, this research proposes a hybrid blockchain-based solution for securely managing EHRs, specifically designed as a framework for tracking inpatient follow-ups. By integrating QR code-enabled data access with a blockchain architecture, this innovative approach enhances… More >

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 >

Shih-Lin Lin^*, Cheng-Wei Li

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-20, 2026, DOI:10.32604/cmc.2025.071915 - 09 December 2025

Abstract This study focuses on developing a deep learning model capable of recognizing vehicle brands and models, integrated with a law enforcement intelligence platform to overcome the limitations of existing license plate recognition techniques—particularly in handling counterfeit, obscured, or absent plates. The research first entailed collecting, annotating, and classifying images of various vehicle models, leveraging image processing and feature extraction methodologies to train the model on Microsoft Custom Vision. Experimental results indicate that, for most brands and models, the system achieves stable and relatively high performance in Precision, Recall, and Average Precision (AP). Furthermore, simulated tests… More >

Hui Wei^1,2, Zhanfei Ma^1,3,*, Jing Jiang¹, Bisheng Wang¹, Zhong Di¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-25, 2026, DOI:10.32604/cmc.2025.069236 - 10 November 2025

Abstract The Internet of Vehicles (IoV) operates in highly dynamic and open network environments and faces serious challenges in secure and real-time authentication and consensus mechanisms. Existing methods often suffer from complex certificate management, inefficient consensus protocols, and poor resilience in high-frequency communication, resulting in high latency, poor scalability, and unstable network performance. To address these issues, this paper proposes a secure and efficient distributed authentication scheme for IoV with reputation-driven consensus and SM9. First, this paper proposes a decentralized authentication architecture that utilizes the certificate-free feature of SM9, enabling lightweight authentication and key negotiation, thereby… More >

Abeer Alnuaim^*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-33, 2026, DOI:10.32604/cmc.2025.069110 - 10 November 2025

Abstract The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification… More >

Dakshnamoorthy Manivannan^*

Computer Systems Science and Engineering, Vol.49, pp. 533-555, 2025, DOI:10.32604/csse.2025.072267 - 08 December 2025

Abstract Attribute-based Encryption (ABE) enhances the confidentiality of Electronic Health Records (EHR) (also known as Personal Health Records (PHR)) by binding access rights not to individual identities, but to user attribute sets such as roles, specialties, or certifications. This data-centric cryptographic paradigm enables highly fine-grained, policy-driven access control, minimizing the need for identity management and supporting scalable multi-user scenarios. This paper presents a comprehensive and critical survey of ABE schemes developed specifically for EHR/PHR systems over the past decade. It explores the evolution of these schemes, analyzing their design principles, strengths, limitations, and the level of More >

Muflih Alhazmi¹, Waqar Ul Hassan², Saba Shaheen³, Mohammed M. A. Almazah⁴, Azmat Ullah Khan Niazi^3,*, Nafisa A. Albasheir⁵, Ameni Gargouri⁶, Naveed Iqbal⁷

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1903-1940, 2025, DOI:10.32604/cmes.2025.072973 - 26 November 2025

Abstract This paper proposes a model-based control framework for vehicle platooning systems with second-order nonlinear dynamics operating over switching signed networks, time-varying delays, and deception attacks. The study includes two configurations: a leaderless structure using Finite-Time Non-Singular Terminal Bipartite Consensus (FNTBC) and Fixed-Time Bipartite Consensus (FXTBC), and a leader—follower structure ensuring structural balance and robustness against deceptive signals. In the leaderless model, a bipartite controller based on impulsive control theory, gauge transformation, and Markovian switching Lyapunov functions ensures mean-square stability and coordination under deception attacks and communication delays. The FNTBC achieves finite-time convergence depending on initial More >

Md Sabir Hossain¹, Md Mahfuzur Rahman^1,2,*, Mufti Mahmud^1,3

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1087-1116, 2025, DOI:10.32604/cmes.2025.068779 - 30 October 2025

Abstract This article presents a human fall detection system that addresses two critical challenges: privacy preservation and detection accuracy. We propose a comprehensive framework that integrates state-of-the-art machine learning models, multimodal data fusion, federated learning (FL), and Karush-Kuhn-Tucker (KKT)-based resource optimization. The system fuses data from wearable sensors and cameras using Gramian Angular Field (GAF) encoding to capture rich spatial-temporal features. To protect sensitive data, we adopt a privacy-preserving FL setup, where model training occurs locally on client devices without transferring raw data. A custom convolutional neural network (CNN) is designed to extract robust features from More > Graphic Abstract

Ahmed El Maloufy, Mohamed Amine Tahiri, Ahmed Bencherqui, Hicham Karmouni, Mhamed Sayyouri^*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5789-5822, 2025, DOI:10.32604/cmc.2025.068885 - 23 October 2025

Abstract In the current digital context, safeguarding copyright is a major issue, particularly for architectural drawings produced by students. These works are frequently the result of innovative academic thinking combining creativity and technical precision. They are particularly vulnerable to the risk of illegal reproduction when disseminated in digital format. This research suggests, for the first time, an innovative approach to copyright protection by embedding a double digital watermark to address this challenge. The solution relies on a synergistic fusion of several sophisticated methods: Krawtchouk Optimized Octonion Moments (OKOM), Quaternion Singular Value Decomposition (QSVD), and Discrete Waveform… More >