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  • Open Access

    ARTICLE

    Intelligent Solution System for Cloud Security Based on Equity Distribution: Model and Algorithms

    Sarah Mustafa Eljack1,*, Mahdi Jemmali2,3,4, Mohsen Denden6,7, Mutasim Al Sadig1, Abdullah M. Algashami1, Sadok Turki5

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 1461-1479, 2024, DOI:10.32604/cmc.2023.040919

    Abstract In the cloud environment, ensuring a high level of data security is in high demand. Data planning storage optimization is part of the whole security process in the cloud environment. It enables data security by avoiding the risk of data loss and data overlapping. The development of data flow scheduling approaches in the cloud environment taking security parameters into account is insufficient. In our work, we propose a data scheduling model for the cloud environment. The model is made up of three parts that together help dispatch user data flow to the appropriate cloud VMs. The first component is the… More >

  • Open Access

    ARTICLE

    On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

    Jinyu Chen1, Long Shi1,*, Qisheng Huang2, Taotao Wang3, Daojing He4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1755-1773, 2024, DOI:10.32604/cmes.2023.046826

    Abstract In permissioned blockchain networks, the Proof of Authority (PoA) consensus, which uses the election of authorized nodes to validate transactions and blocks, has been widely advocated thanks to its high transaction throughput and fault tolerance. However, PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism. As a result, traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network. To address these issues, we propose a novel decentralized reputation management mechanism for permissioned blockchain networks… More > Graphic Abstract

    On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

  • Open Access

    ARTICLE

    A Novel High-Efficiency Transaction Verification Scheme for Blockchain Systems

    Jingyu Zhang1,2, Pian Zhou1, Jin Wang1, Osama Alfarraj3, Saurabh Singh4, Min Zhu5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1613-1633, 2024, DOI:10.32604/cmes.2023.044418

    Abstract Blockchain can realize the reliable storage of a large amount of data that is chronologically related and verifiable within the system. This technology has been widely used and has developed rapidly in big data systems across various fields. An increasing number of users are participating in application systems that use blockchain as their underlying architecture. As the number of transactions and the capital involved in blockchain grow, ensuring information security becomes imperative. Addressing the verification of transactional information security and privacy has emerged as a critical challenge. Blockchain-based verification methods can effectively eliminate the need for centralized third-party organizations. However,… More >

  • Open Access

    ARTICLE

    Efficient DP-FL: Efficient Differential Privacy Federated Learning Based on Early Stopping Mechanism

    Sanxiu Jiao1, Lecai Cai2,*, Jintao Meng3, Yue Zhao3, Kui Cheng2

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 247-265, 2024, DOI:10.32604/csse.2023.040194

    Abstract Federated learning is a distributed machine learning framework that solves data security and data island problems faced by artificial intelligence. However, federated learning frameworks are not always secure, and attackers can attack customer privacy information by analyzing parameters in the training process of federated learning models. To solve the problems of data security and availability during federated learning training, this paper proposes an Efficient Differential Privacy Federated Learning Algorithm based on early stopping mechanism (Efficient DP-FL). This method inherits the advantages of differential privacy and federated learning and improves the performance of model training while protecting the parameter information uploaded… More >

  • Open Access

    ARTICLE

    Cybersecurity Threats Detection Using Optimized Machine Learning Frameworks

    Nadir Omer1,*, Ahmed H. Samak2, Ahmed I. Taloba3,4, Rasha M. Abd El-Aziz3,5

    Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265

    Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

  • Open Access

    ARTICLE

    Enhancing Healthcare Data Security and Disease Detection Using Crossover-Based Multilayer Perceptron in Smart Healthcare Systems

    Mustufa Haider Abidi*, Hisham Alkhalefah, Mohamed K. Aboudaif

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 977-997, 2024, DOI:10.32604/cmes.2023.044169

    Abstract The healthcare data requires accurate disease detection analysis, real-time monitoring, and advancements to ensure proper treatment for patients. Consequently, Machine Learning methods are widely utilized in Smart Healthcare Systems (SHS) to extract valuable features from heterogeneous and high-dimensional healthcare data for predicting various diseases and monitoring patient activities. These methods are employed across different domains that are susceptible to adversarial attacks, necessitating careful consideration. Hence, this paper proposes a crossover-based Multilayer Perceptron (CMLP) model. The collected samples are pre-processed and fed into the crossover-based multilayer perceptron neural network to detect adversarial attacks on the medical records of patients. Once an… More >

  • Open Access

    ARTICLE

    A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

    Lejun Zhang1,2,3,*, Junjie Zhang1, Kentaroh Toyoda4, Yuan Liu2, Jing Qiu2, Zhihong Tian2, Ran Guo5

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 783-800, 2024, DOI:10.32604/cmes.2023.043469

    Abstract Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant… More >

  • Open Access

    REVIEW

    A Review on the Security of the Ethereum-Based DeFi Ecosystem

    Yue Xue1, Dunqiu Fan2, Shen Su1,3,*, Jialu Fu1, Ning Hu1, Wenmao Liu2, Zhihong Tian1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 69-101, 2024, DOI:10.32604/cmes.2023.031488

    Abstract Decentralized finance (DeFi) is a general term for a series of financial products and services. It is based on blockchain technology and has attracted people’s attention because of its open, transparent, and intermediary free. Among them, the DeFi ecosystem based on Ethereum-based blockchains attracts the most attention. However, the current decentralized financial system built on the Ethereum architecture has been exposed to many smart contract vulnerabilities during the last few years. Herein, we believe it is time to improve the understanding of the prevailing Ethereum-based DeFi ecosystem security issues. To that end, we investigate the Ethereum-based DeFi security issues: 1)… More >

  • Open Access

    ARTICLE

    Developing Transparent IDS for VANETs Using LIME and SHAP: An Empirical Study

    Fayaz Hassan1,*, Jianguo Yu1, Zafi Sherhan Syed2, Arif Hussain Magsi3, Nadeem Ahmed4

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3185-3208, 2023, DOI:10.32604/cmc.2023.044650

    Abstract Vehicular Ad-hoc Networks (VANETs) are mobile ad-hoc networks that use vehicles as nodes to create a wireless network. Whereas VANETs offer many advantages over traditional transportation networks, ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks. This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System (IDS) that merges Machine Learning (ML)–based attack detection with Explainable AI (XAI) explanations. This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest (RF) classifier that achieves a classification accuracy of 100% for the binary classification task… More >

  • Open Access

    ARTICLE

    Blockchain-Based Cognitive Computing Model for Data Security on a Cloud Platform

    Xiangmin Guo1,2, Guangjun Liang1,2,*, Jiayin Liu1,2, Xianyi Chen3,*

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3305-3323, 2023, DOI:10.32604/cmc.2023.044529

    Abstract Cloud storage is widely used by large companies to store vast amounts of data and files, offering flexibility, financial savings, and security. However, information shoplifting poses significant threats, potentially leading to poor performance and privacy breaches. Blockchain-based cognitive computing can help protect and maintain information security and privacy in cloud platforms, ensuring businesses can focus on business development. To ensure data security in cloud platforms, this research proposed a blockchain-based Hybridized Data Driven Cognitive Computing (HD2C) model. However, the proposed HD2C framework addresses breaches of the privacy information of mixed participants of the Internet of Things (IoT) in the cloud.… More >

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