Kuldashbay Avazov¹, Jasur Sevinov^2,3, Komil Tashev⁴, Jamila Arzieva⁵, Tulkin Botirov⁶, Alpamis Kutlimuratov⁷, Akmalbek Abdusalomov^{2,4,8,9,10,11}, Boburjon Vafoev¹², Young Im Cho^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087927 - 15 September 2026

Abstract Wireless sensor networks (WSNs) and Internet of Things (IoT) systems require lightweight cryptographic primitives that provide strong security under strict constraints on area, latency, and timing predictability. Ascon, selected by National Institute of Standards and Technology (NIST) as the standard for lightweight authenticated encryption, is well suited for such environments, yet application-oriented hardware implementations for WSN platforms remain underexplored. This paper presents a comprehensive evaluation of field programmable gate array (FPGA)-based Ascon architectures for secure WSN and IoT deployments, using iterative design with single permutation round and hybrid design with two-round unrolling across two FPGA… More >

Lasseni Coulibaly^1,*, Damien Hanyurwimfura¹, Evariste Twahirwa¹, Abubakar Diwani²

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087148 - 15 September 2026

Abstract Intelligent transportation systems, a critical pillar for smart cities, enable real-time vehicular communications to prevent human errors and improve traffic safety and efficiency. However, the open and highly dynamic nature of vehicular networks exposes them to various security threats, including message tampering, impersonation, and privacy violations. Several authentication and key agreement (AKA) protocols have been proposed to mitigate these risks, but often fail to maintain future-proof security against emerging quantum threats or introduce significant latency that affects real-time applications by relying on computationally expensive public-key cryptography, blockchain or centralized architectures. This paper proposes a new… More >

Chun-Hsiu Yeh^1,*, Xian-Zhong Lin^1,*, Yi-Teng Lin¹, Yung-Chen Chou², Wei-Cheng Shen¹

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087068 - 15 September 2026

Abstract Printed circuit board (PCB) defect detection is critical for industrial quality control, where detection models must identify small and irregular defects while satisfying real-time inspection requirements. However, conventional deep learning-based detectors often require substantial computational resources, making deployment on edge devices difficult. To address this issue, FGE-YOLO is proposed as a deployment-oriented lightweight object detection model based on YOLOv8. The proposed model integrates a FasterNet-based backbone, a GhostConv-Based Neck, and an Efficient Channel Attention (ECA) mechanism. In the backbone, standard convolutions are retained in the shallow P1 and P2 stages to preserve low-level spatial details,… More >

Baofeng Duan¹, Xinghai Yu¹, Peng Wang², Tao Feng¹, Yongbo Jiang^1,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.086743 - 15 September 2026

Abstract With the sustained expansion of complex Internet of Things (IoT) ecosystems, malicious traffic detection has become critical for maintaining both cyber security and operational continuity. Modern IoT deployments contain heterogeneous devices, ubiquitous sensing layers, edge services, and autonomous assets, so abnormal communication may affect not only data confidentiality but also physical operations. To address the limitations of independent flow-level detection and heavy graph propagation, this paper proposes a Lightweight Graph-Attentive Network for Traffic Detection (LGNT). LGNT constructs a directed traffic-interaction graph from NetFlow records, where communication entities are represented as nodes and traffic sessions are… More >

Rania Al-Ali¹, Mustafa Al-Fayoumi^1,2, Saleem Alsaraireh^3,*

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085806 - 15 September 2026

Abstract The rapid growth of the Internet of Things (IoT) and edge computing has increased the demand for secure and lightweight image encryption suitable for resource-constrained environments. This paper proposes a hybrid framework combining a residual-based pretrained autoencoder with a novel Cylinder XOR-Cascade (CXC) encryption scheme. The autoencoder compresses images into a compact latent representation while a residual branch preserves fine spatial details for accurate reconstruction. Both representations are encrypted using CXC, a two-pass column-wise stream cipher that enhances confusion and diffusion through sequential SHA3-256-based chaining and a cylinder-like feedback mechanism. Experiments on the USC-SIPI dataset More >

Ronak Patel¹, Miral Patel², Deep Kothadiya³, Bayan AlGhofaily⁴, Faten S. Alamri^5,*, Awad Alyousef⁴, Amjad R Khan⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085703 - 15 September 2026

Abstract Growth of cancerous cells is unpredictable, and their effects vary across organs and levels of aggression. Identification of the pattern, size, and shape of the growth helps assess severity for better treatment. The proposed LiteDKT-Net combines the DK-IRB (Diverse Kernel Inverted Residual Block) block and Transformer to target conceptual information about shape and location. For better edge detection, LiteDKT-Net uses GAG (Group Attention Gate) followed by CBAM (Convolutional Block Attention Module). LiteDKT-Net is a lightweight encoder-decoder-based network optimized for accurate brain tumor segmentation. The network parameter optimization and reduced computational complexity in LiteDKT-Net enable high… More >

Mohammed Atoum¹, Malik Al-Essa^1,*, Yazeed Alsarhan², Ahmad K. Al Hwaitat¹, Muhammad Imran³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085403 - 15 September 2026

Abstract With the urgent need for Intrusion Detection Systems (IDS) to protect digital infrastructure, eXplainable Artificial Intelligence (XAI) has become an important supporting layer. The integration of XAI and IDS can rank influential features that affect IDS decisions, yet these outputs often remain difficult to translate into operational security actions. In this work, we propose LEXIS (LLM-Enhanced eXplainable Intrusion detection System), an LLM-enhanced explainable IDS that converts sample-level explanations into structured report drafts that organize feature attributions into candidate response actions for analyst review, through an evidence-bounded reporting process. Given a network trace, the classifier generates… More >

Jiahui Liu, Longzhen Dong^*, Zeling Hou

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085242 - 15 September 2026

Abstract Detecting surface defects on steel is challenging because many defect regions are visually weak, have blurred boundaries, and contain minimal pixel information. In detectors from the You Only Look Once (YOLO) family, these subtle cues may be weakened at the early feature extraction stage and further attenuated during repeated downsampling. To improve the preservation and utilization of such defect-related details, this paper proposes EFAS-YOLO, a lightweight YOLOv11-based detection framework for steel surface defect inspection. First, an Edge-Frequency Aware Stem (EFAS) is introduced before the backbone to explicitly extract Sobel-based gradient responses and fuse them with… More >

Usman Mohyud Din Chaudhary¹, Humaira Arshad^1,*, Sajid Iqbal^2,*, Abdullah A. Alaulamie², Muhammad Ahsan Raza³, Abid Iqbal⁴

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084774 - 15 September 2026

Abstract Multisignature (multisig) wallets are fundamental to institutional-grade asset security on the Ethereum blockchain, yet Security Operations Centers (SOCs) currently rely on manual threshold rules to flag anomalous executions. Existing anomaly detection approaches suffer from three methodological deficiencies: (i) reliance on random train-test splits that leak future information, (ii) inclusion of post-hoc execution features unavailable at prediction time, and (iii) absence of cross-architectural benchmarking to justify algorithmic choices. This paper addresses all three gaps through a rigorous LightGBM-based framework that automates and explains SOC heuristics. We frame anomaly detection as post-execution forensic triage, where the model… More >

Yazhi Zheng^1,2, Xiaolong Cui^1,*, Xin Wang^1,2,#, Xuanzhu Sheng^1,2,#

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.084480 - 15 September 2026

Abstract Large Language Models (LLMs) currently lack the robust command and decision-making (C&D) capabilities essential for the command and control domain. To address this critical gap, this paper proposes an emergence mechanism that integrates a domain-specialized Chain of Thought (CoT) framework with a Process Reward Model (PRM)-inspired evaluation and inference-time optimization paradigm. We construct a novel Chain of Command and Decision (CoCD) framework, a C2-specific CoT structure with contextual persistence, knowledge accumulation, and a human-in-the-loop feedback loop, and define a four-dimensional PRM-inspired evaluation framework for process-level assessment of C&D reasoning. Experimental evaluations on 40 C&D scenarios… More >