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  • Open Access

    REVIEW

    A Comprehensive Survey on AI-Assisted Multiple Access Enablers for 6G and beyond Wireless Networks

    Kinzah Noor1, Agbotiname Lucky Imoize2,*, Michael Adedosu Adelabu3, Cheng-Chi Lee4,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1575-1664, 2025, DOI:10.32604/cmes.2025.073200 - 26 November 2025

    Abstract The envisioned 6G wireless networks demand advanced Multiple Access (MA) schemes capable of supporting ultra-low latency, massive connectivity, high spectral efficiency, and energy efficiency (EE), especially as the current 5G networks have not achieved the promised 5G goals, including the projected 2000 times EE improvement over the legacy 4G Long Term Evolution (LTE) networks. This paper provides a comprehensive survey of Artificial Intelligence (AI)-enabled MA techniques, emphasizing their roles in Spectrum Sensing (SS), Dynamic Resource Allocation (DRA), user scheduling, interference mitigation, and protocol adaptation. In particular, we systematically analyze the progression of traditional and modern… More > Graphic Abstract

    A Comprehensive Survey on AI-Assisted Multiple Access Enablers for 6G and beyond Wireless Networks

  • Open Access

    REVIEW

    Security and Privacy in Permissioned Blockchain Interoperability: A Systematic Review

    Alsoudi Dua1, Tan Fong Ang1, Chin Soon Ku2,*, Okmi Mohammed1,3, Yu Luo4, Jiahui Chen4, Uzair Aslam Bhatti5, Lip Yee Por1,*

    CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2579-2624, 2025, DOI:10.32604/cmc.2025.070413 - 23 September 2025

    Abstract Blockchain interoperability enables seamless communication and asset transfer across isolated permissioned blockchain systems, but it introduces significant security and privacy vulnerabilities. This review aims to systematically assess the security and privacy landscape of interoperability protocols for permissioned blockchains, identifying key properties, attack vectors, and countermeasures. Using PRISMA 2020 guidelines, we analysed 56 peer-reviewed studies published between 2020 and 2025, retrieved from Scopus, ScienceDirect, Web of Science, and IEEE Xplore. The review focused on interoperability protocols for permissioned blockchains with security and privacy analyses, including only English-language journal articles and conference proceedings. Risk of bias in… More >

  • Open Access

    REVIEW

    Single Sign-On Security and Privacy: A Systematic Literature Review

    Abdelhadi Zineddine1,#, Yousra Belfaik2,#, Abdeslam Rehaimi1, Yassine Sadqi3,*, Said Safi1

    CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4019-4054, 2025, DOI:10.32604/cmc.2025.066139 - 30 July 2025

    Abstract With the proliferation of online services and applications, adopting Single Sign-On (SSO) mechanisms has become increasingly prevalent. SSO enables users to authenticate once and gain access to multiple services, eliminating the need to provide their credentials repeatedly. However, this convenience raises concerns about user security and privacy. The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats, identify gaps in existing systems, and explore effective mitigation solutions. This need motivated the first systematic literature review (SLR) of SSO security and privacy, conducted in… More >

  • Open Access

    ARTICLE

    A Dynamic Social Network Graph Anonymity Scheme with Community Structure Protection

    Yuanjing Hao, Xuemin Wang, Liang Chang*, Long Li, Mingmeng Zhang

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3131-3159, 2025, DOI:10.32604/cmc.2024.059201 - 17 February 2025

    Abstract Dynamic publishing of social network graphs offers insights into user behavior but brings privacy risks, notably re-identification attacks on evolving data snapshots. Existing methods based on -anonymity can mitigate these attacks but are cumbersome, neglect dynamic protection of community structure, and lack precise utility measures. To address these challenges, we present a dynamic social network graph anonymity scheme with community structure protection (DSNGA-CSP), which achieves the dynamic anonymization process by incorporating community detection. First, DSNGA-CSP categorizes communities of the original graph into three types at each timestamp, and only partitions community subgraphs for a specific… More >

  • Open Access

    EDITORIAL

    Introduction to the Special Issue on The Bottleneck of Blockchain Techniques Scalability, Security and Privacy Protection

    Shen Su1,*, Daojing He2, Neeraj Kumar3

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 1933-1937, 2024, DOI:10.32604/cmes.2024.059318 - 31 October 2024

    Abstract This article has no abstract. More >

  • Open Access

    REVIEW

    A Review on Security and Privacy Issues Pertaining to Cyber-Physical Systems in the Industry 5.0 Era

    Abdullah Alabdulatif1, Navod Neranjan Thilakarathne2,*, Zaharaddeen Karami Lawal3,4,*

    CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3917-3943, 2024, DOI:10.32604/cmc.2024.054150 - 12 September 2024

    Abstract The advent of Industry 5.0 marks a transformative era where Cyber-Physical Systems (CPSs) seamlessly integrate physical processes with advanced digital technologies. However, as industries become increasingly interconnected and reliant on smart digital technologies, the intersection of physical and cyber domains introduces novel security considerations, endangering the entire industrial ecosystem. The transition towards a more cooperative setting, including humans and machines in Industry 5.0, together with the growing intricacy and interconnection of CPSs, presents distinct and diverse security and privacy challenges. In this regard, this study provides a comprehensive review of security and privacy concerns pertaining… More >

  • Open Access

    REVIEW

    Security and Privacy Challenges in SDN-Enabled IoT Systems: Causes, Proposed Solutions, and Future Directions

    Ahmad Rahdari1,6, Ahmad Jalili2, Mehdi Esnaashari3, Mehdi Gheisari1,4,7,8,*, Alisa A. Vorobeva5, Zhaoxi Fang1, Panjun Sun1,*, Viktoriia M. Korzhuk5, Ilya Popov5, Zongda Wu1, Hamid Tahaei1

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2511-2533, 2024, DOI:10.32604/cmc.2024.052994 - 15 August 2024

    Abstract Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes.… More >

  • Open Access

    ARTICLE

    Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

    Islam Zada1,*, Mohammed Naif Alatawi2, Syed Muhammad Saqlain1, Abdullah Alshahrani3, Adel Alshamran4, Kanwal Imran5, Hessa Alfraihi6

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835 - 15 August 2024

    Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Security and Privacy Architecture for IoT Networks with the Integration of Blockchain

    Sohaib Latif1,*, M. Saad Bin Ilyas1, Azhar Imran2, Hamad Ali Abosaq3, Abdulaziz Alzubaidi4, Vincent Karovič Jr.5

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 353-379, 2024, DOI:10.32604/iasc.2024.047080 - 21 May 2024

    Abstract The Internet of Things (IoT) is growing rapidly and impacting almost every aspect of our lives, from wearables and healthcare to security, traffic management, and fleet management systems. This has generated massive volumes of data and security, and data privacy risks are increasing with the advancement of technology and network connections. Traditional access control solutions are inadequate for establishing access control in IoT systems to provide data protection owing to their vulnerability to single-point OF failure. Additionally, conventional privacy preservation methods have high latency costs and overhead for resource-constrained devices. Previous machine learning approaches were… More >

  • Open Access

    ARTICLE

    Enhancing Security and Privacy in Distributed Face Recognition Systems through Blockchain and GAN Technologies

    Muhammad Ahmad Nawaz Ul Ghani1, Kun She1,*, Muhammad Arslan Rauf1, Shumaila Khan2, Javed Ali Khan3, Eman Abdullah Aldakheel4, Doaa Sami Khafaga4

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2609-2623, 2024, DOI:10.32604/cmc.2024.049611 - 15 May 2024

    Abstract The use of privacy-enhanced facial recognition has increased in response to growing concerns about data security and privacy in the digital age. This trend is spurred by rising demand for face recognition technology in a variety of industries, including access control, law enforcement, surveillance, and internet communication. However, the growing usage of face recognition technology has created serious concerns about data monitoring and user privacy preferences, especially in context-aware systems. In response to these problems, this study provides a novel framework that integrates sophisticated approaches such as Generative Adversarial Networks (GANs), Blockchain, and distributed computing… More >

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