Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Le Thi Hong Van^1,*, Le Duc Thuan¹, Pham Van Huong¹, Nguyen Hieu Minh²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075027 - 10 February 2026

Abstract Optimizing convolutional neural networks (CNNs) for IoT attack detection remains a critical yet challenging task due to the need to balance multiple performance metrics beyond mere accuracy. This study proposes a unified and flexible optimization framework that leverages metaheuristic algorithms to automatically optimize CNN configurations for IoT attack detection. Unlike conventional single-objective approaches, the proposed method formulates a global multi-objective fitness function that integrates accuracy, precision, recall, and model size (speed/model complexity penalty) with adjustable weights. This design enables both single-objective and weighted-sum multi-objective optimization, allowing adaptive selection of optimal CNN configurations for diverse deployment… 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 >

Bo Zhu^#, Enzhi Dong^#, Zhonghua Cheng^*, Kexin Jiang, Chiming Guo, Shuai Yue

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.074009 - 10 February 2026

Abstract Accurate prediction of remaining useful life serves as a reliable basis for maintenance strategies, effectively reducing both the frequency of failures and associated costs. As a core component of PHM, RUL prediction plays a crucial role in preventing equipment failures and optimizing maintenance decision-making. However, deep learning models often falter when processing raw, noisy temporal signals, fail to quantify prediction uncertainty, and face challenges in effectively capturing the nonlinear dynamics of equipment degradation. To address these issues, this study proposes a novel deep learning framework. First, a new bidirectional long short-term memory network integrated with More >

Sghaier Guizani^1,*, Tehseen Mazhar^2,3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073883 - 10 February 2026

Abstract The advent of quantum computing poses a significant challenge to traditional cryptographic protocols, particularly those used in Secure Multiparty Computation (MPC), a fundamental cryptographic primitive for privacy-preserving computation. Classical MPC relies on cryptographic techniques such as homomorphic encryption, secret sharing, and oblivious transfer, which may become vulnerable in the post-quantum era due to the computational power of quantum adversaries. This study presents a review of 140 peer-reviewed articles published between 2000 and 2025 that used different databases like MDPI, IEEE Explore, Springer, and Elsevier, examining the applications, types, and security issues with the solution of… More >

Zeyuan Chen, Yafei Xie, Jinkun Li, Song Wang, Yingqiang Ding^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073846 - 10 February 2026

Abstract Stereo matching is a pivotal task in computer vision, enabling precise depth estimation from stereo image pairs, yet it encounters challenges in regions with reflections, repetitive textures, or fine structures. In this paper, we propose a Semantic-Guided Parallax Attention Stereo Matching Network (SGPASMnet) that can be trained in unsupervised manner, building upon the Parallax Attention Stereo Matching Network (PASMnet). Our approach leverages unsupervised learning to address the scarcity of ground truth disparity in stereo matching datasets, facilitating robust training across diverse scene-specific datasets and enhancing generalization. SGPASMnet incorporates two novel components: a Cross-Scale Feature Interaction… More >

Sicheng Ma¹, Lixiang Zhang^2,*, Guocai Chen³, Zeyu Dai³, Junru Zhu⁴, Wei Fang^1,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073572 - 10 February 2026

Abstract Community detection is a fundamental problem in network analysis for identifying densely connected node clusters, with successful applications in diverse fields like social networks, recommendation systems, biology, and cyberattack detection. Overlapping community detection refers to the case of a node belonging to multiple communities simultaneously, which is a much more meaningful and challenging task. Graph representation learning with Evolutionary Computation has been studied well in overlapping community detection to deal with complex network structures and characteristics. However, most of them focus on searching the entire solution space, which can be inefficient and lead to inadequate… More >

Qasem Abu Al-Haija^1,*, Shahad Al Tamimi²

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073540 - 10 February 2026

Abstract Adversarial Reinforcement Learning (ARL) models for intelligent devices and Network Intrusion Detection Systems (NIDS) improve system resilience against sophisticated cyber-attacks. As a core component of ARL, Adversarial Training (AT) enables NIDS agents to discover and prevent new attack paths by exposing them to competing examples, thereby increasing detection accuracy, reducing False Positives (FPs), and enhancing network security. To develop robust decision-making capabilities for real-world network disruptions and hostile activity, NIDS agents are trained in adversarial scenarios to monitor the current state and notify management of any abnormal or malicious activity. The accuracy and timeliness of… More >

Boram Kim, Guebin Choi^*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073500 - 10 February 2026

Abstract Traditional anomaly detection methods often assume that data points are independent or exhibit regularly structured relationships, as in Euclidean data such as time series or image grids. However, real-world data frequently involve irregular, interconnected structures, requiring a shift toward non-Euclidean approaches. This study introduces a novel anomaly detection framework designed to handle non-Euclidean data by modeling transactions as graph signals. By leveraging graph convolution filters, we extract meaningful connection strengths that capture relational dependencies often overlooked in traditional methods. Utilizing the Graph Convolutional Networks (GCN) framework, we integrate graph-based embeddings with conventional anomaly detection models, More >

Lu Rong^1,#, Manyu Xu^2,3,#, Wenbo Zhu^2,*, Zhihao Yang^2,3, Chao Dong^1,4,5, Yunzhi Zhang^2,3, Kai Wang^1,2, Bing Zheng^1,4,5

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.073442 - 10 February 2026

Abstract Deep learning has become integral to robotics, particularly in tasks such as robotic grasping, where objects often exhibit diverse shapes, textures, and physical properties. In robotic grasping tasks, due to the diverse characteristics of the targets, frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy, which presents a significant challenge for non-experts. Neural Architecture Search (NAS) provides a compelling method through the automated generation of network architectures, enabling the discovery of models that achieve high accuracy through efficient search algorithms. Compared to manually designed networks, NAS methods… More >