Mudassir Khalil¹, Muhammad Imran Sharif^2,*, Ahmed Naeem³, Muhammad Umar Chaudhry¹, Hafiz Tayyab Rauf^4,*, Adham E. Ragab⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1461-1461, 2025, DOI:10.32604/cmc.2024.061589 - 03 January 2025

Abstract This article has no abstract. More >

Sarvenaz Sadat Khatami¹, Mehrdad Shoeibi², Anita Ershadi Oskouei³, Diego Martín^4,*, Maral Keramat Dashliboroun⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 881-911, 2025, DOI:10.32604/cmc.2024.059999 - 03 January 2025

Abstract The Internet of Things (IoT) is integral to modern infrastructure, enabling connectivity among a wide range of devices from home automation to industrial control systems. With the exponential increase in data generated by these interconnected devices, robust anomaly detection mechanisms are essential. Anomaly detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate patterns. This paper presents a novel approach utilizing generative adversarial networks (GANs) for anomaly detection in IoT systems. However, optimizing GANs involves tuning hyper-parameters such as learning rate, batch size, and optimization algorithms,… More >

Huilin Jia¹, Shanqiao Huang¹, Zifeng Yuan^1,2,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 193-222, 2025, DOI:10.32604/cmc.2024.059950 - 03 January 2025

Abstract In this manuscript, we propose an analytical equivalent linear viscoelastic constitutive model for fiber-reinforced composites, bypassing general computational homogenization. The method is based on the reduced-order homogenization (ROH) approach. The ROH method typically involves solving multiple finite element problems under periodic conditions to evaluate elastic strain and eigenstrain influence functions in an ‘off-line’ stage, which offers substantial cost savings compared to direct computational homogenization methods. Due to the unique structure of the fibrous unit cell, “off-line” stage calculation can be eliminated by influence functions obtained analytically. Introducing the standard solid model to the ROH method More >

Nazreen Banu A^*, S.K.B. Sangeetha

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 579-607, 2025, DOI:10.32604/cmc.2024.059805 - 03 January 2025

Abstract The increasing use of cloud-based devices has reached the critical point of cybersecurity and unwanted network traffic. Cloud environments pose significant challenges in maintaining privacy and security. Global approaches, such as IDS, have been developed to tackle these issues. However, most conventional Intrusion Detection System (IDS) models struggle with unseen cyberattacks and complex high-dimensional data. In fact, this paper introduces the idea of a novel distributed explainable and heterogeneous transformer-based intrusion detection system, named INTRUMER, which offers balanced accuracy, reliability, and security in cloud settings by multiple modules working together within it. The traffic captured… More >

Balsam Dhyia Majeed^1,2, Amir Hossein Taherinia^1,*, Hadi Sadoghi Yazdi¹, Ahad Harati¹

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1027-1047, 2025, DOI:10.32604/cmc.2024.059789 - 03 January 2025

Abstract Watermarking is embedding visible or invisible data within media to verify its authenticity or protect copyright. The watermark is embedded in significant spatial or frequency features of the media to make it more resistant to intentional or unintentional modification. Some of these features are important perceptual features according to the human visual system (HVS), which means that the embedded watermark should be imperceptible in these features. Therefore, both the designers of watermarking algorithms and potential attackers must consider these perceptual features when carrying out their actions. The two roles will be considered in this paper… More >

Ange Chen, Chengdong Wu^*, Chuanjiang Leng

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 173-191, 2025, DOI:10.32604/cmc.2024.059284 - 03 January 2025

Abstract Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly, meaning that skeleton structure information is not utilized and multi-view pose information is not completely fused. Moreover, existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs, making the correlation weights between nodes in the graph and their neighborhood nodes shared. Existing Graph Convolutional Networks (GCNs) cannot extract global and deep-level skeleton structure information and view… More >

Ali Alqarni^*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 389-406, 2025, DOI:10.32604/cmc.2024.059238 - 03 January 2025

Abstract Open networks and heterogeneous services in the Internet of Vehicles (IoV) can lead to security and privacy challenges. One key requirement for such systems is the preservation of user privacy, ensuring a seamless experience in driving, navigation, and communication. These privacy needs are influenced by various factors, such as data collected at different intervals, trip durations, and user interactions. To address this, the paper proposes a Support Vector Machine (SVM) model designed to process large amounts of aggregated data and recommend privacy-preserving measures. The model analyzes data based on user demands and interactions with service More >

Hafida Assmi¹, Azidine Guezzaz¹, Said Benkirane¹, Mourade Azrour^2,*, Said Jabbour³, Nisreen Innab⁴, Abdulatif Alabdulatif⁵

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 443-466, 2025, DOI:10.32604/cmc.2024.059047 - 03 January 2025

Abstract Internet of Things (IoT) refers to the infrastructures that connect smart devices to the Internet, operating autonomously. This connectivity makes it possible to harvest vast quantities of data, creating new opportunities for the emergence of unprecedented knowledge. To ensure IoT securit, various approaches have been implemented, such as authentication, encoding, as well as devices to guarantee data integrity and availability. Among these approaches, Intrusion Detection Systems (IDS) is an actual security solution, whose performance can be enhanced by integrating various algorithms, including Machine Learning (ML) and Deep Learning (DL), enabling proactive and accurate detection of… More >

Suheib Alhiyari, Siti Hafizah AB Hamid^*, Nur Nasuha Daud

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1087-1107, 2025, DOI:10.32604/cmc.2024.058953 - 03 January 2025

Abstract Link failure is a critical issue in large networks and must be effectively addressed. In software-defined networks (SDN), link failure recovery schemes can be categorized into proactive and reactive approaches. Reactive schemes have longer recovery times while proactive schemes provide faster recovery but overwhelm the memory of switches by flow entries. As SDN adoption grows, ensuring efficient recovery from link failures in the data plane becomes crucial. In particular, data center networks (DCNs) demand rapid recovery times and efficient resource utilization to meet carrier-grade requirements. This paper proposes an efficient Decentralized Failure Recovery (DFR) model… More >

Hongxing Wang¹, Xilai Ju², Hua Zhu^1,*, Huafeng Li^1,*

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract