Chunqian Guo^*, Gang Chen

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2025.072184

Abstract Despite the ability of the anonymous communication system The Onion Router (Tor) to obscure the content of communications, prior studies have shown that passive adversaries can still infer the websites visited by users through website fingerprinting (WF) attacks. Conventional WF methodologies demonstrate optimal performance in scenarios involving single-tab browsing. Conventional WF methods achieve optimal performance primarily in scenarios involving single-tab browsing. However, in real-world network environments, users often engage in multi-tab browsing, which generates overlapping traffic patterns from different websites. This overlap has been shown to significantly degrade the performance of classifiers that rely on… More >

Rafika Harrabi^1,2,*, Slim Ben Chaabane^1,2, Hassene Seddik²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.072054

Abstract Securing restricted zones such as airports, research facilities, and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets. Face recognition has emerged as a key biometric approach for this purpose; however, existing systems are often sensitive to variations in illumination, occlusion, and pose, which degrade their performance in real-world conditions. To address these challenges, this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant (FG-2DFLD), Generalized 2D Linear Discriminant Analysis (G2DLDA), and Modular-Local Binary Patterns (Modular-LBP)… More >

Jingjing Liu¹, Chuanyang Liu^1,2,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075348

Abstract Intelligent inspection of transmission lines enables efficient automated fault detection by integrating artificial intelligence, robotics, and other related technologies. It plays a key role in ensuring power grid safety, reducing operation and maintenance costs, driving the digital transformation of the power industry, and facilitating the achievement of the dual-carbon goals. This review focuses on vision-based power line inspection, with deep learning as the core perspective to systematically analyze the latest research advancements in this field. Firstly, at the technical foundation level, it elaborates on deep learning algorithms for intelligent transmission line inspection based on image… More >

Nurbek Sissenov^1,*, Gulden Ulyukova^1,*, Dina Satybaldina², Nikolaj Goranin³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075504

Abstract The reliability of information systems (IS) is a key factor in the sustainable operation of modern digital services. However, existing assessment methods remain fragmented and are often limited to individual indicators or expert judgments. This paper proposes a hybrid methodology for a comprehensive assessment of IS reliability based on the integration of the international standard ISO/IEC 25010:2023, multicriteria analysis methods (ARAS, CoCoSo, and TOPSIS), and the XGBoost machine learning algorithm for missing data imputation. The structure of the ISO/IEC 25010 standard is used to formalize reliability criteria and subcriteria, while the AHP method allows for… More >

Trong-Thua Huynh^1,*, De-Thu Huynh², Du-Thang Phu¹, Hong-Son Nguyen¹, Quoc H. Nguyen³

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076623

Abstract This paper introduces MobiIris, a lightweight deep network for mobile iris recognition that enhances attention and specifically addresses the balance between accuracy and efficiency on devices with limited resources. The proposed model is based on the large version of MobileNetV3 and adds more spatial attention blocks and an embedding-based head that was trained using margin-based triplet learning, enabling fine-grained modeling of iris textures in a compact representation. To further improve discriminability, we design a training pipeline that combines dynamic-margin triplet loss, a staged hard/semi-hard negative mining strategy, and feature-level knowledge distillation from a ResNet-50 teacher.… More >

Yanming Chen¹, Fuxiang Yuan^2,*, Zekang Wang²

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075072

Abstract Network topology obfuscation is a technique aimed at protecting critical nodes and links from disruptions such as Link Flooding Attack (LFA). Currently, there are limited topology obfuscation methods for protecting critical nodes, and the existing approaches mainly achieve obfuscation by extensively modifying network links, resulting in high costs. To address this issue, this paper proposes a low-cost network topology obfuscation method dedicated to critical node protection, with its core innovation lying in a lightweight obfuscation architecture based on Fake Node Clusters (FNCs). Firstly, the protected network is modeled as an undirected graph, and an adjacency… More >

Hui Chen¹, Mohammed A. H. Ali^1,*, Bushroa Abd Razak¹, Zhenya Wang², Yusoff Nukman¹, Shikai Zhang¹, Zhiwei Huang¹, Ligang Yao³, Mohammad Alkhedher⁴

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.065539

Abstract Most existing path planning approaches rely on discrete expansions or localized heuristics that can lead to extended re-planning, inefficient detours, and limited adaptability to complex obstacle distributions. These issues are particularly pronounced when navigating cluttered or large-scale environments that demand both global coverage and smooth trajectory generation. To address these challenges, this paper proposes a Wave Water Simulator (WWS) algorithm, leveraging a physically motivated wave equation to achieve inherently smooth, globally consistent path planning. In WWS, wavefront expansions naturally identify safe corridors while seamlessly avoiding local minima, and selective corridor focusing reduces computational overhead in More >

Malik Al-Essa^1,*, Mohammad Qatawneh^2,1, Ahmad Sami Al-Shamayleh³, Orieb Abualghanam¹, Wesam Almobaideen^4,1

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076608

Abstract Machine Learning (ML) intrusion detection systems (IDS) are vulnerable to manipulations: small, protocol-valid manipulations can push samples across brittle decision boundaries. We study two complementary remedies that reshape the learner in distinct ways. Adversarial Training (AT) exposes the model to worst-case, in-threat perturbations during learning to thicken local margins; Counterfactual Augmentation (CF-Aug) adds near-boundary exemplars that are explicitly constrained to be feasible, causally consistent, and operationally meaningful for defenders. The main goal of this work is to investigate and compare how AT and CF-Aug can reshape the decision surface of the IDS. eXplainable Artificial Intelligence More >

Adeel Iqbal^1,#, Tahir Khurshaid^2,#, Syed Abdul Mannan Kirmani³, Mohammad Arif^4,*, Muhammad Faisal Siddiqui^5,*

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075819

Abstract Vehicular Internet of Things (V-IoT) networks need intelligent and adaptive spectrum access methods for ensuring ultra-reliable and low-latency communication (URLLC) in highly dynamic environments. Traditional reinforcement learning (RL)-based algorithms, such as Q-Learning and Double Q-Learning, are often characterized by unstable convergence and inefficient exploration in the presence of stochastic vehicular traffic and interference. This paper proposes Adaptive Reinforcement Q-learning with Upper Confidence Bound (ARQ-UCB), a lightweight and reliability-aware RL framework, which explicitly reduces interruption and blocking probabilities while improving throughput and delay across diverse vehicular traffic conditions. This proposed ARQ-UCB algorithm extends the basic Q-updates More >

Somia A. Abd El-Mottaleb¹, Amira G. Mohamed², Mehtab Singh³, Hassan Yousif Ahmed⁴, Medien Zeghid⁴, Abu Sufian A. Osman^5,*, Sami Mourou⁵

CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075669

Abstract This paper presents an image encryption scheme for underwater optical wireless communication (UOWC) systems based on dynamically generated hyperchaotic S-boxes, aiming to enhance both data security and transmission performance in underwater environments. The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics. Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8 (7.9925–7.9993), with very low correlation coefficients in horizontal, vertical, and diagonal directions, as well as the system achieves high values in key… More >