Jiacheng Xiong¹, Xingshu Chen^1,2,3,*, Xiao Lan^2,3, Liangguo Chen^1,2

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2273-2303, 2025, DOI:10.32604/cmc.2025.063984 - 03 July 2025

Abstract In the era of big data, the growing number of real-time data streams often contains a lot of sensitive privacy information. Releasing or sharing this data directly without processing will lead to serious privacy information leakage. This poses a great challenge to conventional privacy protection mechanisms (CPPM). The existing data partitioning methods ignore the number of data replications and information exchanges, resulting in complex distance calculations and inefficient indexing for high-dimensional data. Therefore, CPPM often fails to meet the stringent requirements of efficiency and reliability, especially in dynamic spatiotemporal environments. Addressing this concern, we proposed… More >

Yu-Bin Kim, Dong-Hyuk Shin, Ieck-Chae Euom^*

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2217-2243, 2025, DOI:10.32604/cmc.2025.063289 - 03 July 2025

Abstract The global surge in electric vehicle (EV) adoption is proportionally expanding the EV charging station (EVCS) infrastructure, thereby increasing the attack surface and potential impact of security breaches within this critical ecosystem. While ISO 15118 standardizes EV-EVCS communication, its underspecified security guidelines and the variability in manufacturers’ implementations frequently result in vulnerabilities that can disrupt charging services, compromise user data, or affect power grid stability. This research introduces a systematic black-box fuzzing methodology, accompanied by an open-source tool, to proactively identify and mitigate such security flaws in EVCS firmware operating under ISO 15118. The proposed… More >

Jing Wang¹, Shuanghu Fang^1,*, Zihua Li², Shaoyong Ma³

International Journal of Mental Health Promotion, Vol.27, No.6, pp. 845-861, 2025, DOI:10.32604/ijmhp.2025.061476 - 30 June 2025

Abstract Objectives: College students face increasing mental health challenges. Although Acceptance and Commitment Therapy (ACT) is effective, the efficacy of Internet-based ACT (iACT) needs further exploration. Methods: This study examines the efficacy of iACT on college students’ mental health through a randomized controlled trial. We recruited 90 college students (19.16 ± 1.02 years old) and randomly divided them into the iACT group, face-to-face ACT group, and control group. The effects of the interventions were evaluated using the comprehensive assessment of ACT processes (CompACT) and the Depression Anxiety Stress Scales (DASS-21). Results: Two-factor repeated measures ANOVA revealed a… More >

Omar Alnajar^*, Ahmed Barnawi

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3861-3897, 2025, DOI:10.32604/cmes.2025.065833 - 30 June 2025

Abstract The Tactile Internet of Things (TIoT) promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems. Yet TIoT’s stringent requirements for ultra-low latency, high reliability, and robust privacy present significant challenges. Conventional centralized Federated Learning (FL) architectures struggle with latency and privacy constraints, while fully distributed FL (DFL) faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous. To address these limitations, we propose a Clustered Distributed Federated Learning (CDFL) architecture tailored for a 6G-enabled TIoT environment. Clients are grouped into clusters based on… More >

Mohammed S. Alshehri^1,*, Oumaima Saidani², Wajdan Al Malwi³, Fatima Asiri³, Shahid Latif ⁴, Aizaz Ahmad Khattak⁵, Jawad Ahmad⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 3899-3920, 2025, DOI:10.32604/cmes.2025.064874 - 30 June 2025

Abstract The emergence of Generative Adversarial Network (GAN) techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems (IDS). However, conventional GAN-based IDS models face several challenges, including training instability, high computational costs, and system failures. To address these limitations, we propose a Hybrid Wasserstein GAN and Autoencoder Model (WGAN-AE) for intrusion detection. The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model. The model was trained and evaluated using two recent benchmark datasets, 5GNIDD and IDSIoT2024. When trained on the 5GNIDD dataset,… More >

Ronghua Wen¹, Lan Luo^2,*, Shiping Luo³, Tianlin Chen², Meng Fan¹

Journal of Psychology in Africa, Vol.35, No.2, pp. 231-239, 2025, DOI:10.32604/jpa.2025.065770 - 30 June 2025

Abstract This study investigated the relationship between social anxiety and problematic Internet use in college students, and how it is moderated by attitudes toward seeking professional psychological help. Participants were 1451 Chinese college students (female = 60.2%; mean age = 19.85 years, SD = 1.89 years). They completed the Interaction Anxiousness Scale, the Attitudes Toward Seeking Professional Psychological Help Scale-Short Form, and the Problematic Internet Use Scale. The results revealed that college students with higher social anxiety reported greater severity of problematic Internet use. Moreover, students with negative attitudes toward seeking professional psychological help also reported More >

Alireza Bahmani^*

Journal on Internet of Things, Vol.7, pp. 1-17, 2025, DOI:10.32604/jiot.2025.060786 - 24 June 2025

Abstract With the development of the Internet of Things (IoT) technology and its widespread integration in various aspects of life, the risks associated with cyberattacks on these systems have increased significantly. Vulnerabilities in IoT devices, stemming from insecure designs and software weaknesses, have made attacks on them more complex and dangerous compared to traditional networks. Conventional intrusion detection systems are not fully capable of identifying and managing these risks in the IoT environment, making research and evaluation of suitable intrusion detection systems for IoT crucial. In this study, deep learning, multi-layer perceptron (MLP), Random Forest (RF),… More >

Menwa Alshammeri¹, Mamoona Humayun^2,*, Khalid Haseeb³, Ghadah Naif Alwakid¹

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 433-446, 2025, DOI:10.32604/cmc.2025.065660 - 09 June 2025

Abstract Wireless technologies and the Internet of Things (IoT) are being extensively utilized for advanced development in traditional communication systems. This evolution lowers the cost of the extensive use of sensors, changing the way devices interact and communicate in dynamic and uncertain situations. Such a constantly evolving environment presents enormous challenges to preserving a secure and lightweight IoT system. Therefore, it leads to the design of effective and trusted routing to support sustainable smart cities. This research study proposed a Genetic Algorithm sentiment-enhanced secured optimization model, which combines big data analytics and analysis rules to evaluate… More >

Xuan Wu¹, Yafei Song¹, Xiaodan Wang^1,*, Peng Wang¹, Qian Xiang²

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1279-1305, 2025, DOI:10.32604/cmc.2025.064471 - 09 June 2025

Abstract With the growth of the Internet of Things (IoT) comes a flood of malicious traffic in the IoT, intensifying the challenges of network security. Traditional models operate with independent layers, limiting their effectiveness in addressing these challenges. To address this issue, we propose a cross-layer cooperative Feature Subset-Based Malicious Traffic Detection (FSMMTD) model for detecting malicious traffic. Our approach begins by applying an enhanced random forest method to adaptively filter and retain highly discriminative first-layer features. These processed features are then input into an improved state-space model that integrates the strengths of recurrent neural networks… More >

Jiaqi Wang, Jian Su^*

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 125-152, 2025, DOI:10.32604/cmc.2025.064309 - 09 June 2025

Abstract The Intelligent Transportation System (ITS), as a vital means to alleviate traffic congestion and reduce traffic accidents, demonstrates immense potential in improving traffic safety and efficiency through the integration of Internet of Things (IoT) technologies. The enhancement of its performance largely depends on breakthrough advancements in object detection technology. However, current object detection technology still faces numerous challenges, such as accuracy, robustness, and data privacy issues. These challenges are particularly critical in the application of ITS and require in-depth analysis and exploration of future improvement directions. This study provides a comprehensive review of the development… More >