Tianqing Yuan^1,2, Na Li^1,2, Hao Sun³, Sen Tan^4,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4497-4512, 2024, DOI:10.32604/cmc.2024.056061

Abstract To improve the estimation accuracy of state of charge (SOC) and state of health (SOH) for lithium-ion batteries, in this paper, a joint estimation method of SOC and SOH at charging cut-off voltage based on genetic algorithm (GA) combined with back propagation (BP) neural network is proposed, the research addresses the issue of data manipulation resulting from cyber-attacks. Firstly, anomalous data stemming from cyber-attacks are identified and eliminated using the isolated forest algorithm, followed by data restoration. Secondly, the incremental capacity (IC) curve is derived from the restored data using the Kalman filtering algorithm, with… More >

Ali Asghar^1,#, Amna Shifa^2,#, Mamoona Naveed Asghar^2,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3591-3635, 2024, DOI:10.32604/cmc.2024.054654

Abstract Videos represent the most prevailing form of digital media for communication, information dissemination, and monitoring. However, their widespread use has increased the risks of unauthorised access and manipulation, posing significant challenges. In response, various protection approaches have been developed to secure, authenticate, and ensure the integrity of digital videos. This study provides a comprehensive survey of the challenges associated with maintaining the confidentiality, integrity, and availability of video content, and examining how it can be manipulated. It then investigates current developments in the field of video security by exploring two critical research questions. First, it… More >

Ahmad Alzu’bi^1,*, Amjad Albashayreh², Abdelrahman Abuarqoub³, Mai A. M. Alfawair⁴

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3785-3802, 2024, DOI:10.32604/cmc.2024.052599

Abstract In the era of the Internet of Things (IoT), the proliferation of connected devices has raised security concerns, increasing the risk of intrusions into diverse systems. Despite the convenience and efficiency offered by IoT technology, the growing number of IoT devices escalates the likelihood of attacks, emphasizing the need for robust security tools to automatically detect and explain threats. This paper introduces a deep learning methodology for detecting and classifying distributed denial of service (DDoS) attacks, addressing a significant security concern within IoT environments. An effective procedure of deep transfer learning is applied to utilize More >

Borja Bordel Sánchez^1,*, Ramón Alcarria², Tomás Robles¹

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349

Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

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

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 >

Yangrong Chen^1,2, June Li^3,*, Yu Xia³, Ruiwen Zhang³, Lingling Li^1,2, Xiaoyu Li^1,2, Lin Ge^1,2

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2579-2609, 2024, DOI:10.32604/cmc.2024.053230

Abstract Intelligent electronic devices (IEDs) are interconnected via communication networks and play pivotal roles in transmitting grid-related operational data and executing control instructions. In the context of the heightened security challenges within smart grids, IEDs pose significant risks due to inherent hardware and software vulnerabilities, as well as the openness and vulnerability of communication protocols. Smart grid security, distinct from traditional internet security, mainly relies on monitoring network security events at the platform layer, lacking an effective assessment mechanism for IEDs. Hence, we incorporate considerations for both cyber-attacks and physical faults, presenting security assessment indicators and… More > Graphic Abstract

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

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 >

Singamaneni Krishnapriya^*, Sukhvinder Singh

CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2675-2719, 2024, DOI:10.32604/cmc.2024.052447

Abstract The increase in number of people using the Internet leads to increased cyberattack opportunities. Advanced Persistent Threats, or APTs, are among the most dangerous targeted cyberattacks. APT attacks utilize various advanced tools and techniques for attacking targets with specific goals. Even countries with advanced technologies, like the US, Russia, the UK, and India, are susceptible to this targeted attack. APT is a sophisticated attack that involves multiple stages and specific strategies. Besides, TTP (Tools, Techniques, and Procedures) involved in the APT attack are commonly new and developed by an attacker to evade the security system.… More >

Nisreen Innab¹, Ahmed Abdelgader Fadol Osman², Mohammed Awad Mohammed Ataelfadiel², Marwan Abu-Zanona^3,*, Bassam Mohammad Elzaghmouri⁴, Farah H. Zawaideh⁵, Mouiad Fadeil Alawneh⁶

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1325-1345, 2024, DOI:10.32604/cmc.2024.051778

Abstract Phishing, an Internet fraud where individuals are deceived into revealing critical personal and account information, poses a significant risk to both consumers and web-based institutions. Data indicates a persistent rise in phishing attacks. Moreover, these fraudulent schemes are progressively becoming more intricate, thereby rendering them more challenging to identify. Hence, it is imperative to utilize sophisticated algorithms to address this issue. Machine learning is a highly effective approach for identifying and uncovering these harmful behaviors. Machine learning (ML) approaches can identify common characteristics in most phishing assaults. In this paper, we propose an ensemble approach… More >

Hong Huang, Yunfei Wang^*, Guotao Yuan, Xin Li

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 361-387, 2024, DOI:10.32604/cmc.2024.051633

Abstract Deep Neural Networks (DNNs) are integral to various aspects of modern life, enhancing work efficiency. Nonetheless, their susceptibility to diverse attack methods, including backdoor attacks, raises security concerns. We aim to investigate backdoor attack methods for image categorization tasks, to promote the development of DNN towards higher security. Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples, and the meticulous data screening by developers, hindering practical attack implementation. To overcome these challenges, this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation (GN-TUAP) algorithm. This approach… More >