Chengqi Liu¹, Yongtao Li², Weiping Zou^3,*, Deyu Lin^4,5,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074975 - 10 February 2026

Abstract With the large-scale deployment of the Internet of Things (IoT) devices, their weak security mechanisms make them prime targets for malware attacks. Attackers often use Domain Generation Algorithm (DGA) to generate random domain names, hiding the real IP of Command and Control (C&C) servers to build botnets. Due to the randomness and dynamics of DGA, traditional methods struggle to detect them accurately, increasing the difficulty of network defense. This paper proposes a lightweight DGA detection model based on knowledge distillation for resource-constrained IoT environments. Specifically, a teacher model combining CharacterBERT, a bidirectional long short-term memory More >

Enzo Hoummady^*, Fehmi Jaafar

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074897 - 10 February 2026

Abstract With the proliferation of Internet of Things (IoT) devices, securing these interconnected systems against cyberattacks has become a critical challenge. Traditional security paradigms often fail to cope with the scale and diversity of IoT network traffic. This paper presents a comparative benchmark of classic machine learning (ML) and state-of-the-art deep learning (DL) algorithms for IoT intrusion detection. Our methodology employs a two-phased approach: a preliminary pilot study using a custom-generated dataset to establish baselines, followed by a comprehensive evaluation on the large-scale CICIoTDataset2023. We benchmarked algorithms including Random Forest, XGBoost, CNN, and Stacked LSTM. The… More >

Thi-Xuyen Bui^1,2, Yu-Sheng Lu¹, Yu-Sheng Liao¹, Te-Hua Fang^1,3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074862 - 10 February 2026

Abstract The mechanical performance of exceedingly soft materials such as Ag is significantly influenced by various working conditions. Therefore, this study systematically investigates the effects of crack geometry, substrate crystal orientation, and indenter shape on crack propagation. The mechanical response of Ag is analyzed using the quasi-continuum (QC) method. A pre-crack with a predefined depth and angle was introduced to initiate fracture behavior. The results show that when the pre-crack height is 50 Å, the crack propagates rapidly as the imprint depth increases from 0 to 7 Å, grows steadily up to 15 Å, and then… More >

Shereen S. Jumaa¹, Mohsin H. Challoob², Amjad J. Humaidi^2,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074752 - 10 February 2026

Abstract Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access. This paper proposes a new Tri-independent keying method for encrypting military images. The proposed encryption method is based on multilevel security stages of pixel-level scrambling, bit-level manipulation, and block-level shuffling operations. For having a vast key space, the input password is hashed by the Secure Hash Algorithm 256-bit (SHA-256) for generating independently deterministic keys used in the multilevel stages. A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive… More >

Lishu Qin^*, Yu Gao, Xinyuan Lu

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074739 - 10 February 2026

Abstract With the rapid development of intelligent navigation technology, efficient and safe path planning for mobile robots has become a core requirement. To address the challenges of complex dynamic environments, this paper proposes an intelligent path planning framework based on grid map modeling. First, an improved Safe and Smooth A* (SSA*) algorithm is employed for global path planning. By incorporating obstacle expansion and corner-point optimization, the proposed SSA* enhances the safety and smoothness of the planned path. Then, a Partitioned Dynamic Window Approach (PDWA) is integrated for local planning, which is triggered when dynamic or sudden… More >

Wenhe Chen¹, Yue Wang¹, Shuonan Shen¹, Leer Hua¹, Caixia Zheng², Qi Pu^1,*, Xundiao Ma^3,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074682 - 10 February 2026

Abstract In fire rescue scenarios, traditional manual operations are highly dangerous, as dense smoke, low visibility, extreme heat, and toxic gases not only hinder rescue efficiency but also endanger firefighters’ safety. Although intelligent rescue robots can enter hazardous environments in place of humans, smoke poses major challenges for human detection algorithms. These challenges include the attenuation of visible and infrared signals, complex thermal fields, and interference from background objects, all of which make it difficult to accurately identify trapped individuals. To address this problem, we propose VIF-YOLO, a visible–infrared fusion model for real-time human detection in… More >

Yingli Liu^1,2, Weiyong Tang^1,2, Xiao Yang^1,2, Jiancheng Yin^3,*, Haihe Zhou^1,2

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2026.074681 - 10 February 2026

Abstract Quantitative analysis of aluminum-silicon (Al-Si) alloy microstructure is crucial for evaluating and controlling alloy performance. Conventional analysis methods rely on manual segmentation, which is inefficient and subjective, while fully supervised deep learning approaches require extensive and expensive pixel-level annotated data. Furthermore, existing semi-supervised methods still face challenges in handling the adhesion of adjacent primary silicon particles and effectively utilizing consistency in unlabeled data. To address these issues, this paper proposes a novel semi-supervised framework for Al-Si alloy microstructure image segmentation. First, we introduce a Rotational Uncertainty Correction Strategy (RUCS). This strategy employs multi-angle rotational perturbations… More >

Bassam Al-Shargabi^1,*, Mohammed Abbas Fadhil Al-Husainy², Abdelrahman Abuarqoub¹, Omar Albahbouh Aldabbas³

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074550 - 10 February 2026

Abstract The advent of 5G technology has significantly enhanced the transmission of images over networks, expanding data accessibility and exposure across various applications in digital technology and social media. Consequently, the protection of sensitive data has become increasingly critical. Regardless of the complexity of the encryption algorithm used, a robust and highly secure encryption key is essential, with randomness and key space being crucial factors. This paper proposes a new Robust Deoxyribonucleic Acid (RDNA) nucleotide-based encryption method. The RDNA encryption method leverages the unique properties of DNA nucleotides, including their inherent randomness and extensive key space,… More >

Shu-Yin Chiang^*, Shin-En Huang

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074528 - 10 February 2026

Abstract This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping (SLAM), real-time object recognition, and dynamic obstacle avoidance. The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping, fusing geometric and visual data to build a high-fidelity 2D semantic map. This map enables the robot to identify and project object information for improved situational awareness. Experimental results show that object recognition reached 95.4% mAP@0.5. Semantic completeness increased from 68.7% (single view) to 94.1% (multi-view) with an More >

Zhendong Du^*, Hanliu Wang, Kenji Hashimoto

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074520 - 10 February 2026

Abstract Planning in lexical-prior-free environments presents a fundamental challenge for evaluating whether large language models (LLMs) possess genuine structural reasoning capabilities beyond lexical memorization. When predicates and action names are replaced with semantically irrelevant random symbols while preserving logical structures, existing direct generation approaches exhibit severe performance degradation. This paper proposes a symbol-agnostic closed-loop planning pipeline that enables models to construct executable plans through systematic validation and iterative refinement. The system implements a complete generate-verify-repair cycle through six core processing components: semantic comprehension extracts structural constraints, language planner generates text plans, symbol translator performs structure-preserving mapping,… More >