Tong Wu^*, Dawei Zhou

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3067-3094, 2025, DOI:10.32604/cmc.2025.066525 - 23 September 2025

Abstract Fault attacks have emerged as an increasingly effective approach for integrated circuit security attacks due to their short execution time and minimal data requirement. However, the lack of a unified leakage model remains a critical challenge, as existing methods often rely on algorithm-specific details or prior knowledge of plaintexts and intermediate values. This paper proposes the Fault Probability Model based on Hamming Weight (FPHW) to address this. This novel statistical framework quantifies fault attacks by solely analyzing the statistical response of the target device, eliminating the need for attack algorithm details or implementation specifics. Building… More >

Mohammad Nowsin Amin Sheikh^1,2,*, Muhammad Saibtain Raza¹, I-Shyan Hwang^1,*, Md. Alamgir Hossain³, Ihsan Ullah¹, Tahmid Hasan⁴, Mohammad Syuhaimi Ab-Rahman⁵

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 4043-4066, 2025, DOI:10.32604/cmc.2025.065850 - 23 September 2025

Abstract The rapid advancement of the Internet of Things (IoT) has heightened the importance of security, with a notable increase in Distributed Denial-of-Service (DDoS) attacks targeting IoT devices. Network security specialists face the challenge of producing systems to identify and offset these attacks. This research manages IoT security through the emerging Software-Defined Networking (SDN) standard by developing a unified framework (RNN-RYU). We thoroughly assess multiple deep learning frameworks, including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Feed-Forward Convolutional Neural Network (FFCNN), and Recurrent Neural Network (RNN), and present the novel usage of Synthetic Minority Over-Sampling More >

Ghadah Aldehim, Shakila Basheer, Ala Saleh Alluhaidan, Sapiah Sakri^*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3599-3619, 2025, DOI:10.32604/cmc.2025.065643 - 23 September 2025

Abstract Although digital changes in power systems have added more ways to monitor and control them, these changes have also led to new cyber-attack risks, mainly from False Data Injection (FDI) attacks. If this happens, the sensors and operations are compromised, which can lead to big problems, disruptions, failures and blackouts. In response to this challenge, this paper presents a reliable and innovative detection framework that leverages Bidirectional Long Short-Term Memory (Bi-LSTM) networks and employs explanatory methods from Artificial Intelligence (AI). Not only does the suggested architecture detect potential fraud with high accuracy, but it also… More >

Junzhe Cheng^*

Journal of Cyber Security, Vol.7, pp. 343-357, 2025, DOI:10.32604/jcs.2025.067979 - 22 September 2025

Abstract We present a novel Transformer-based network intrusion detection system (IDS) that automatically learns complex feature relationships from raw traffic. Our architecture embeds both categorical (e.g., protocol, flag) and numerical (e.g., packet count, duration) inputs into a unified latent space with positional encodings, and processes them through multi-layer multi-head self-attention blocks. The Transformer’s global attention enables the IDS to capture subtle, long-range correlations in the data (e.g., coordinated multi-step attacks) without manual feature engineering. We complement the model with extensive data augmentation (SMOTE, GANs) to mitigate class imbalance and improve robustness. In evaluation on benchmark datasets… More >

Asad Khan¹, Fathia Moh. Al Samman^2,*, Waqar Ul Hassan³, Mohammed M. A. Almazah⁴, A. Y. Al-Rezami⁵, Azmat Ullah Khan Niazi^3,*, Adnan Manzor⁶

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 1971-1999, 2025, DOI:10.32604/cmes.2025.068240 - 31 August 2025

Abstract This research paper tackles the complexities of achieving global fuzzy consensus in leader-follower systems in robotic systems, focusing on robust control systems against an advanced signal attack that integrates sensor and actuator disturbances within the dynamics of follower robots. Each follower robot has unknown dynamics and control inputs, which expose it to the risks of both sensor and actuator attacks. The leader robot, described by a second-order, time-varying nonlinear model, transmits its position, velocity, and acceleration information to follower robots through a wireless connection. To handle the complex setup and communication among robots in the… More > Graphic Abstract

Kyle Stein^1,*, Andrew A. Mahyari^1,2, Guillermo Francia III³, Eman El-Sheikh³

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1001-1018, 2025, DOI:10.32604/cmc.2025.068201 - 29 August 2025

Abstract Deep neural networks (DNNs) and generative AI (GenAI) are increasingly vulnerable to backdoor attacks, where adversaries embed triggers into inputs to cause models to misclassify or misinterpret target labels. Beyond traditional single-trigger scenarios, attackers may inject multiple triggers across various object classes, forming unseen backdoor-object configurations that evade standard detection pipelines. In this paper, we introduce DBOM (Disentangled Backdoor-Object Modeling), a proactive framework that leverages structured disentanglement to identify and neutralize both seen and unseen backdoor threats at the dataset level. Specifically, DBOM factorizes input image representations by modeling triggers and objects as independent primitives in the… More >

Xiaohan Chen¹, Yuanfang Chen^1,*, Gyu Myoung Lee², Noel Crespi³, Pierluigi Siano⁴

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1733-1750, 2025, DOI:10.32604/cmc.2025.067284 - 29 August 2025

Abstract Graph Neural Networks (GNNs) have demonstrated outstanding capabilities in processing graph-structured data and are increasingly being integrated into large-scale pre-trained models, such as Large Language Models (LLMs), to enhance structural reasoning, knowledge retrieval, and memory management. The expansion of their application scope imposes higher requirements on the robustness of GNNs. However, as GNNs are applied to more dynamic and heterogeneous environments, they become increasingly vulnerable to real-world perturbations. In particular, graph data frequently encounters joint adversarial perturbations that simultaneously affect both structures and features, which are significantly more challenging than isolated attacks. These disruptions, caused… More >

Chaimae Hazman^1,2, Azidine Guezzaz², Said Benkirane², Mourade Azrour^3,*, Vinayakumar Ravi⁴, Abdulatif Alabdulatif ⁵

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 301-329, 2025, DOI:10.32604/cmc.2025.064777 - 29 August 2025

Abstract Attacks are growing more complex and dangerous as network capabilities improve at a rapid pace. Network intrusion detection is usually regarded as an efficient means of dealing with security attacks. Many ways have been presented, utilizing various strategies and focusing on different types of visitors. Anomaly-based network intrusion monitoring is an essential area of intrusion detection investigation and development. Despite extensive research on anomaly-based network detection, there is still a lack of comprehensive literature reviews covering current methodologies and datasets. Despite the substantial research into anomaly-based network intrusion detection algorithms, there is a dearth of More >

Michael Juma Ayuma¹, Shem Mbandu Angolo^1,*, Philemon Nthenge Kasyoka^2,*

Journal on Artificial Intelligence, Vol.7, pp. 205-254, 2025, DOI:10.32604/jai.2025.067361 - 19 August 2025

Abstract The exponential growth of Internet of Things (IoT) devices has introduced significant security challenges, particularly in securing token-based communication protocols used for authentication and authorization. This survey systematically reviews the vulnerabilities in token transmission within IoT environments, focusing on various sophisticated attack vectors such as replay attacks, token hijacking, man-in-the-middle (MITM) attacks, token injection, and eavesdropping among others. These attacks exploit the inherent weaknesses of token-based mechanisms like OAuth, JSON Web Tokens (JWT), and bearer tokens, which are widely used in IoT ecosystems for managing device interactions and access control. The impact of such attacks… More >

Hyeong-Gyeong Kim¹, Sang-Min Choi², Hyeon Seo², Suwon Lee^2,*

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4381-4397, 2025, DOI:10.32604/cmc.2025.067024 - 30 July 2025

Abstract Adversarial attacks pose a significant threat to artificial intelligence systems by exposing them to vulnerabilities in deep learning models. Existing defense mechanisms often suffer drawbacks, such as the need for model retraining, significant inference time overhead, and limited effectiveness against specific attack types. Achieving perfect defense against adversarial attacks remains elusive, emphasizing the importance of mitigation strategies. In this study, we propose a defense mechanism that applies random cropping and Gaussian filtering to input images to mitigate the impact of adversarial attacks. First, the image was randomly cropped to vary its dimensions and then placed… More >