Zaed Mahdi^1,*, Nada Abdalhussien², Naba Mahmood¹, Rana Zaki^3,*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2139-2159, 2024, DOI:10.32604/cmc.2024.053542 - 15 August 2024

Abstract The primary concern of modern technology is cyber attacks targeting the Internet of Things. As it is one of the most widely used networks today and vulnerable to attacks. Real-time threats pose with modern cyber attacks that pose a great danger to the Internet of Things (IoT) networks, as devices can be monitored or service isolated from them and affect users in one way or another. Securing Internet of Things networks is an important matter, as it requires the use of modern technologies and methods, and real and up-to-date data to design and train systems… More >

Wufei Wu¹, Javad Hassannataj Joloudari^2,3,4, Senthil Kumar Jagatheesaperumal⁵, Kandala N. V. P. S. Rajesh⁶, Silvia Gaftandzhieva^7,*, Sadiq Hussain⁸, Rahimullah Rabih⁹, Najibullah Haqjoo¹⁰, Mobeen Nazar¹¹, Hamed Vahdat-Nejad⁹, Rositsa Doneva¹²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2785-2813, 2024, DOI:10.32604/cmc.2024.053037 - 15 August 2024

Abstract The high performance of IoT technology in transportation networks has led to the increasing adoption of Internet of Vehicles (IoV) technology. The functional advantages of IoV include online communication services, accident prevention, cost reduction, and enhanced traffic regularity. Despite these benefits, IoV technology is susceptible to cyber-attacks, which can exploit vulnerabilities in the vehicle network, leading to perturbations, disturbances, non-recognition of traffic signs, accidents, and vehicle immobilization. This paper reviews the state-of-the-art achievements and developments in applying Deep Transfer Learning (DTL) models for Intrusion Detection Systems in the Internet of Vehicles (IDS-IoV) based on anomaly… More >

Ahmad Rahdari^1,6, Ahmad Jalili², Mehdi Esnaashari³, Mehdi Gheisari^1,4,7,8,*, Alisa A. Vorobeva⁵, Zhaoxi Fang¹, Panjun Sun^1,*, 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 >

Hina Gul¹, Sana Ullah¹, Ki-Il Kim^2,*, Farman Ali³

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1831-1850, 2024, DOI:10.32604/cmc.2024.052841 - 15 August 2024

Abstract The seamless integration of intelligent Internet of Things devices with conventional wireless sensor networks has revolutionized data communication for different applications, such as remote health monitoring, industrial monitoring, transportation, and smart agriculture. Efficient and reliable data routing is one of the major challenges in the Internet of Things network due to the heterogeneity of nodes. This paper presents a traffic-aware, cluster-based, and energy-efficient routing protocol that employs traffic-aware and cluster-based techniques to improve the data delivery in such networks. The proposed protocol divides the network into clusters where optimal cluster heads are selected among super… More >

Abdulrahman Alzahrani^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2331-2349, 2024, DOI:10.32604/cmc.2024.052804 - 15 August 2024

Abstract The recent development of the Internet of Things (IoTs) resulted in the growth of IoT-based DDoS attacks. The detection of Botnet in IoT systems implements advanced cybersecurity measures to detect and reduce malevolent botnets in interconnected devices. Anomaly detection models evaluate transmission patterns, network traffic, and device behaviour to detect deviations from usual activities. Machine learning (ML) techniques detect patterns signalling botnet activity, namely sudden traffic increase, unusual command and control patterns, or irregular device behaviour. In addition, intrusion detection systems (IDSs) and signature-based techniques are applied to recognize known malware signatures related to botnets.… More >

Qiuyan Wang, Haibing Dong^*, Yongfei Huang, Zenglei Liu, Yundong Gou

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 1967-1983, 2024, DOI:10.32604/cmc.2024.052775 - 15 August 2024

Abstract Sharing data while protecting privacy in the industrial Internet is a significant challenge. Traditional machine learning methods require a combination of all data for training; however, this approach can be limited by data availability and privacy concerns. Federated learning (FL) has gained considerable attention because it allows for decentralized training on multiple local datasets. However, the training data collected by data providers are often non-independent and identically distributed (non-IID), resulting in poor FL performance. This paper proposes a privacy-preserving approach for sharing non-IID data in the industrial Internet using an FL approach based on blockchain… More >

Dayong Wang^1,*, Kamalrulnizam Bin Abu Bakar¹, Babangida Isyaku²

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2065-2080, 2024, DOI:10.32604/cmc.2024.051944 - 15 August 2024

Abstract The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC’s computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot,… More >

Nisreen Innab^*

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2237-2258, 2024, DOI:10.32604/cmc.2024.049608 - 15 August 2024

Abstract Internet of Health Things (IoHT) is a subset of Internet of Things (IoT) technology that includes interconnected medical devices and sensors used in medical and healthcare information systems. However, IoHT is susceptible to cybersecurity threats due to its reliance on low-power biomedical devices and the use of open wireless channels for communication. In this article, we intend to address this shortcoming, and as a result, we propose a new scheme called, the certificateless anonymous authentication (CAA) scheme. The proposed scheme is based on hyperelliptic curve cryptography (HECC), an enhanced variant of elliptic curve cryptography (ECC)… More >

Sameer Qazi^1,*, Muhammad Bilal Kadri², Muhammad Naveed^1,*, Bilal A. Khawaja³, Sohaib Zia Khan⁴, Muhammad Mansoor Alam^5,6,7, Mazliham Mohd Su’ud⁶

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 3289-3314, 2024, DOI:10.32604/cmc.2024.048893 - 15 August 2024

Abstract COVID-19 pandemic restrictions limited all social activities to curtail the spread of the virus. The foremost and most prime sector among those affected were schools, colleges, and universities. The education system of entire nations had shifted to online education during this time. Many shortcomings of Learning Management Systems (LMSs) were detected to support education in an online mode that spawned the research in Artificial Intelligence (AI) based tools that are being developed by the research community to improve the effectiveness of LMSs. This paper presents a detailed survey of the different enhancements to LMSs, which… More >

Md Zahir Ahmed^1,*, Oli Ahmed², Lingfeng Gao¹, Mary C. Jobe³, Weijian Li¹

International Journal of Mental Health Promotion, Vol.26, No.7, pp. 517-529, 2024, DOI:10.32604/ijmhp.2024.051055 - 30 July 2024

Abstract In recent years, speculation of an increase in Internet Gaming Disorder (IGD) has surfaced with the growing popularity of internet gaming among Chinese children and adolescents. The detrimental impact of IGD on mental health cannot be denied, even though only a small portion of the screen-dependent population exhibits psychopathological and behavioral symptoms. The present study aimed to explore a latent profile analysis (LPA) of Internet Gaming Disorder on the mental health of Chinese school students. The data were collected from a sample of 1005 Chinese school students (49.8% male; age M = 13.32, SD = 1.34… More >