Lin Huang^1,2, Ying Zhou², Jingjing Yang^1,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4587-4606, 2025, DOI:10.32604/cmc.2025.068370 - 23 October 2025

Abstract The construction of centralized and standardized material databases is essential to support both scientific innovation and industrial application. However, for rare and precious metal materials, existing data resources are often decentralized. This results in persistent issues such as data silos and fragmentation, which significantly hinder efficient data utilization and collaboration. In response to these challenges, this study investigates the development of an integrated platform for sharing genetic data of rare and precious metal materials. The research begins by analyzing current trends in material data platforms, both domestically and internationally. These insights help inform the architectural… More >

Jingfu Yan, Huachun Zhou^*, Weilin Wang

Computer Systems Science and Engineering, Vol.49, pp. 499-531, 2025, DOI:10.32604/csse.2025.068151 - 10 October 2025

Abstract The 6G network architecture introduces the paradigm of Trust + Security, representing a shift in network protection strategies from external defense mechanisms to endogenous security enforcement. While ZTNs (zero-trust networks) have demonstrated significant advancements in constructing trust-centric frameworks, most existing ZTN implementations lack comprehensive integration of security deployment and traffic monitoring capabilities. Furthermore, current ZTN designs generally do not facilitate dynamic assessment of user reputation. To address these limitations, this study proposes a DPZTN (Data-plane-based Zero Trust Network). DPZTN framework extends traditional ZTN models by incorporating security mechanisms directly into the data plane. Additionally, blockchain infrastructure… More > Graphic Abstract

Raj Sonani¹, Reham Alhejaili^2,*, Pushpalika Chatterjee³, Khalid Hamad Alnafisah⁴, Jehad Ali^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3169-3189, 2025, DOI:10.32604/cmes.2025.070225 - 30 September 2025

Abstract Healthcare networks are transitioning from manual records to electronic health records, but this shift introduces vulnerabilities such as secure communication issues, privacy concerns, and the presence of malicious nodes. Existing machine and deep learning-based anomalies detection methods often rely on centralized training, leading to reduced accuracy and potential privacy breaches. Therefore, this study proposes a Blockchain-based-Federated Learning architecture for Malicious Node Detection (BFL-MND) model. It trains models locally within healthcare clusters, sharing only model updates instead of patient data, preserving privacy and improving accuracy. Cloud and edge computing enhance the model’s scalability, while blockchain ensures More >

Puranam Revanth Kumar¹, Gouse Baig Mohammad², Pallati Narsimhulu³, Dharnisha Narasappa⁴, Lakshmana Phaneendra Maguluri⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 2779-2818, 2025, DOI:10.32604/cmes.2025.066365 - 30 September 2025

Abstract Blockchain Technology (BT) has emerged as a transformative solution for improving the efficacy, security, and transparency of supply chain intelligence. Traditional Supply Chain Management (SCM) systems frequently have problems such as data silos, a lack of visibility in real time, fraudulent activities, and inefficiencies in tracking and traceability. Blockchain’s decentralized and irreversible ledger offers a solid foundation for dealing with these issues; it facilitates trust, security, and the sharing of data in real-time among all parties involved. Through an examination of critical technologies, methodology, and applications, this paper delves deeply into computer modeling based-blockchain framework… More >

Alsoudi Dua¹, Tan Fong Ang¹, Chin Soon Ku^2,*, Okmi Mohammed^1,3, Yu Luo⁴, Jiahui Chen⁴, Uzair Aslam Bhatti⁵, Lip Yee Por^1,*

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 >

Asrar Mahboob¹, Muhammad Rashad¹, Ghulam Abbas¹, Zohaib Mushtaq², Tehseen Mazhar^3,*, Ateeq Ur Rehman^4,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3805-3823, 2025, DOI:10.32604/cmc.2025.067615 - 23 September 2025

Abstract This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three factors, Quantum-resistant feature engineering using the UNSW-NB15 dataset adapted for solar infrastructure anomalies. An enhanced Light Gradient Boosting Machine (LightGBM) classifier with blockchain-validated decision thresholds, and A cryptographic proof-of-threat (PoT) consensus mechanism for cyber attack verification. The proposed Immutable LightGBM model with majority voting and cryptographic feature encoding achieves 96.9% detection accuracy with 0.97 weighted average of precision, recall and F1-score, outperforming conventional intrusion detection systems (IDSs) by… More >

Enas W. Abood¹, Ali A. Yassin^2,*, Zaid Ameen Abduljabbar^2,3,4,*, Vincent Omollo Nyangaresi^5,6, Iman Qays Abduljaleel², Abdulla J. Y. Aldarwish², Husam A. Neamah^7,8

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 2305-2345, 2025, DOI:10.32604/cmc.2025.066579 - 23 September 2025

Abstract The digital revolution era has impacted various domains, including healthcare, where digital technology enables access to and control of medical information, remote patient monitoring, and enhanced clinical support based on the Internet of Health Things (IoHTs). However, data privacy and security, data management, and scalability present challenges to widespread adoption. This paper presents a comprehensive literature review that examines the authentication mechanisms utilized within IoHT, highlighting their critical roles in ensuring secure data exchange and patient privacy. This includes various authentication technologies and strategies, such as biometric and multi-factor authentication, as well as the influence More >

Amal Al-Rasheed¹, Hashim Ali^2,*, Rahim Khan^2,*, Aamir Saeed³

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3419-3436, 2025, DOI:10.32604/cmc.2025.065156 - 23 September 2025

Abstract In the domain of Electronic Medical Records (EMRs), emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy. This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data. By leveraging the decentralized and immutable nature of blockchain, the proposed approach ensures transparency, integrity, and traceability of EMR transactions, effectively mitigating risks of unauthorized access and data tampering. Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions, eliminating reliance on intermediaries and reducing the potential for… More >

Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849 - 31 August 2025

Abstract The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which… More >

Singamaneni Krishnapriya^1,2,*, Sukhvinder Singh¹

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1849-1881, 2025, DOI:10.32604/cmes.2025.068326 - 31 August 2025

Abstract The explosive expansion of the Internet of Things (IoT) systems has increased the imperative to have strong and robust solutions to cyber Security, especially to curtail Distributed Denial of Service (DDoS) attacks, which can cripple critical infrastructure. The proposed framework presented in the current paper is a new hybrid scheme that induces deep learning-based traffic classification and blockchain-enabled mitigation to make intelligent, decentralized, and real-time DDoS countermeasures in an IoT network. The proposed model fuses the extracted deep features with statistical features and trains them by using traditional machine-learning algorithms, which makes them more accurate… More > Graphic Abstract