Open Access
ARTICLE
Shoeb Ali Syed1, Zohaib Mushtaq2,*, Akbare Yaqub3, Saifur Rahman4, Muhammad Irfan4, Saleh Al Dawsari4,5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076869
Abstract Blockchain networks are under mounting pressure from emerging complex zero-day attacks that cannot be prevented with conventional security measures. In this paper, we introduce NeuroChain Sentinel, a new bio-inspired cybersecurity model based on spiking neural networks for detecting anomalies in a distributed ledger system in real time. The main innovations are: a Temporal Spike Pattern Recognition algorithm for simulating the biological timing of the neural system to detect malicious transaction patterns; a distributed consensus-verification topology combined with blockchain algorithms; and small-scale neuromorphic engineering, resulting in an 87% reduction in computational load over conventional deep neural… More >
Open Access
REVIEW
Lingyu Jin1,2, Rui Wang1,2, Bo Huang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.080050
Abstract Unmanned Aerial Vehicle (UAV) target tracking is one of the key technologies in aerial intelligent perception systems, playing a vital role in applications such as traffic monitoring, border patrol, disaster response, search and rescue, environmental monitoring, and military reconnaissance. Compared with generic object tracking tasks, UAV platforms exhibit significant differences in imaging perspectives, target scales, motion patterns, and onboard computing capabilities, which pose unique challenges for UAV target tracking, including small targets and drastic scale variations, platform motion and motion blur, complex backgrounds and frequent occlusions, low-light conditions at night, as well as real-time and… More >
Open Access
ARTICLE
Xian Wu, Liang Wan*, Jingxia Ren, Bangfeng Zhang
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077928
（This article belongs to the Special Issue: Advances in Intrusion Detection and Prevention Systems)
Abstract Traditional malware detection models rely on a single feature source for detection, resulting in high false positive or false negative rates due to incomplete information. In addition, conventional models depend on manual feature engineering, which is inefficient and hard to adapt to new malware variants. To address these challenges, this paper proposes a malware detection model called WAFDect based on a self-attention mechanism with multi-source feature fusion. The model consists of two key designs. First, we construct a multi-source feature extraction model that analyzes multi-source data such as API call sequences, registry operation logs, file… More >
Open Access
REVIEW
Yutong Guo1,2, Jianying Yang1,3, Chao Yang1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077820
Abstract Large language models (LLMs) and related foundation-model workflows are emerging as promising tools for advancing foundry intelligence across the casting value chain. This review examines their applications in material design and property prediction, process parameter optimization and intelligent control, and defect detection and quality tracing in casting environments. The surveyed studies indicate that LLM-enabled systems can help integrate unstructured technical knowledge with multimodal industrial data. This integration supports composition design, simulation-assisted process optimization, diagnostic reasoning, and knowledge-grounded decision support. However, current evidence shows that the transition from pilot demonstrations to robust industrial deployment remains constrained More >
Open Access
ARTICLE
Sarmad Dheyaa Azeez1, Saadaldeen Rashid Ahmed2,3, Muhammad Ilyas4,*, Abu Saleh Musa Miah5, Fahmid Al Farid6,7,*, Md. Hezerul Abdul Karim6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076283
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract Traditional Intrusion Detection Systems (IDSs) that rely on fixed signatures or basic machine learning often struggle with sophisticated, multi-stage cyberattacks and previously unknown threats. To fix these problems, this paper introduces IntrusionNet, a mixed deep learning system that combines Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Autoencoders in a two-part design. Differing from typical stacked models, IntrusionNet works on two levels at the same time. First, a supervised CNN-RNN process pulls spatial-temporal data from traffic flows to sort well-known attack patterns. Second, an unsupervised Autoencoder process spots new anomalies by looking at reconstruction… More >
Open Access
ARTICLE
Qian Rong1, Lu Zhang2, Ling Yuan1,*, Zhong Yang3, Guohui Li3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075102
Abstract Federated learning (FL) enables collaborative model training across decentralized clients without sharing raw data, thereby preserving privacy. However, in real-world FL deployments—such as sensor-based activity recognition, wearable health monitoring, and industrial Internet of Things, where local training data often suffer from heterogeneous noisy labels due to diverse collection environments, sensor limitations, and labeling errors. These noisy labels, typically distributed unevenly across clients due to differences in client-side annotation, exacerbate Non-Independent and Identically Distributed (non-IID) data issues, leading to biased updates, unstable convergence, and degraded global model performance. Accurate estimation of client-specific noise rates is therefore… More >
Open Access
ARTICLE
Jinsung Park1,#, Jaehyuk Lee1,2,#, Eunchan Kim1,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079734
Abstract Accurate short-term load forecasting is essential for reliable power system operation, particularly under the increasing uncertainty caused by abnormal weather and socio-economic fluctuations. This study presents a month-conditioned boosting framework that integrates SHapley Additive Explanations (SHAPs) into model refinement. A baseline XGBoost model was first compared with linear and tree-based regressors, followed by enhancements through lagged and rolling-window features as well as loss weighting for vulnerable months. To further improve the performance, SHAP analysis was employed to identify the dominant error-contributing features, which guided the construction of targeted month-specific interaction terms for retraining. Experimental results More >
Open Access
ARTICLE
Raza Hasan*, Shakeel Ahmad, Ismet Gocer, Zakirul Bhuiyan
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079708
（This article belongs to the Special Issue: Artificial Intelligence in Visual and Audio Signal Processing)
Abstract Maritime Domain Awareness (MDA) is critical for global security and economic stability, yet it is increasingly challenged by sophisticated adversarial tactics such as signal spoofing and “dark vessel” activities. Traditional surveillance systems, often reliant on single-sensor modalities, are ill-equipped to handle these deceptive behaviors. To address this, we propose the Multimodal Attention-based Fusion Transformer (MAFT), a novel deep learning architecture that integrates four distinct data modalities—Aerial imagery, Synthetic Aperture Radar (SAR), acoustic signatures, and Automatic Identification System (AIS) data—to achieve robust and interpretable maritime anomaly detection. A key contribution of our work is a principled… More >
Graphic Abstract
Open Access
ARTICLE
Soumalya De1, Rahil Akhtar2, Saiyed Umer2, Ranjeet Kumar Rout3, G. G. Md. Nawaz Ali4,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079340
（This article belongs to the Special Issue: Sentiment Analysis for Social Media Data: Lexicon-Based and Large Language Model Approaches)
Abstract This work presents a comprehensive sustainable sentiment analysis system utilizing textual data, designed within a structured client-server architecture for real-time deployment. The system integrates dual feature representations Bag-of-Words (BoW) and Term Frequency–Inverse Document Frequency (TF-IDF) whose prediction scores are combined through a parameter-free score-level fusion strategy. The implementation of the proposed system consists of five major components. The first component involves the acquisition of textual data from various sources, followed by rigorous text preprocessing to eliminate noise and enhance data quality. The second component focuses on feature extraction, ensuring that the extracted features not only… More >
Open Access
ARTICLE
Liping Wang1, Jiang Wu1,2,*, Liang Wang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078591
Abstract The rapid growth of networked systems and the increasing diversity of cyberattack behaviors have posed significant challenges to intrusion detection, particularly in scenarios characterized by high-dimensional features and severe class imbalance. Conventional detection approaches based on handcrafted rules or shallow representations often exhibit limited robustness under such conditions. To address these issues, this paper presents a hybrid deep learning framework for network intrusion detection that integrates complementary feature learning mechanisms within a dual-branch architecture. Specifically, a Transformer branch is employed to model long-range temporal dependencies in network traffic, while a convolutional neural network branch (CNN)… More >
Open Access
ARTICLE
Tao Bai1, Yang Tang2, Kuan Shao3, Zhenyong Zhang3,*, Yuanteng Liu4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078473
Abstract In recent years, the rapid development of artificial intelligence has greatly promoted the application of Machine Learning as a Service (MLaaS). Users can upload their requirements through front-end applications, and the server provides model inference services after receiving the user input. However, MLaaS may lead to serious privacy breaches. Large language model services are typical representatives of MLaaS, and the Transformer is a typical structure in large language models. Therefore, this paper proposes a privacy-protected Transformer inference scheme based on the CKKS fully homomorphic encryption scheme to optimize computational and communication efficiency. Firstly, this paper… More >
Open Access
REVIEW
Muhammet Ali Karabulut1, A. F. M. Shahen Shah2, Al-Sakib Khan Pathan3,*, Phillip G. Bradford4
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077625
Abstract Today, technological progress is broad and deep. The next generation networks and systems will integrate features, technologies, and models requiring smooth cooperation between new and old technologies. This survey’s uniqueness is that it considers an integrated, hybrid and heterogeneous future where Internet of Things (IoT), Sixth-Generation (6G) mobile communications technology, and Artificial Intelligence (AI) will work together, providing a smart and connected Intelligent Transportation System (ITS). This smart ITS will give better road safety and optimized travel. Currently, there is a scarcity of surveys focusing particularly on smart ITS that is expected soon. In this More >
Open Access
ARTICLE
Jianbin Li1,*, Hang Bao1, Xin Tong2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076676
（This article belongs to the Special Issue: Privacy-Preserving and Secure Federated Learning for IoT, Cyber-Physical, and Maritime Systems)
Abstract The proliferation of large-scale graph data has enabled Graph Neural Networks (GNNs) to achieve significant success in domains such as recommender systems, social network analysis, and biomedicine. However, in practical networked environments, particularly in distributed service infrastructures, graph data is often isolated between multiple edge smart devices and cannot be shared due to privacy, making GNN models weak in generalization. Subgraph Federated Learning (SFL) mitigates this challenge by treating local client data as subgraphs of the global graph to decentralized GNN training. Unfortunately, client-side missing edges make GNN model difficult to capture dependency information between… More >
Open Access
ARTICLE
Jia-Rou Lin1, Chun-Hsiung Tseng1,*, Hao-Chiang Koong Lin2, Andrew Chih-Wei Huang3
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079379
（This article belongs to the Special Issue: AI-Powered Software Engineering)
Abstract Effective pair programming relies heavily on optimal partner compatibility, a requirement that is often difficult to scale manually in software engineering education. This study presents the empirical validation of Codenote, an AI-driven Integrated Development Environment (IDE) designed to automate personality-aware group formation. By integrating a behavioral analysis mechanism, Codenote infers student personality traits from coding patterns to construct complementary pairs, thereby facilitating intelligent collaborative learning. To validate the system’s effectiveness, a controlled experiment was conducted to assess the impact of this AI-mediated pairing strategy on students’ self-efficacy across adaptive, innovative, and persuasive domains. Results indicate… More >
Open Access
ARTICLE
Haodong Zou1,*, Yichen Zhao1, Xin Chen1, Ling Wang1, Jinghang Yu1, Long Yuan2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077908
（This article belongs to the Special Issue: Multimodal Learning for Big Data)
Abstract Root cause analysis (RCA), which leverages multi-modal observability data (including metrics, traces, and logs) to identify the fundamental source of system failures, is critical for ensuring the reliability of complex microservice systems. Traditionally, RCA has relied on human engineers to manually correlate these fragmented signals, which is a labor-intensive and error-prone process. Although recent AIOps advancements, particularly those leveraging Large Language Models (LLMs), aim to automate this workflow, they remain constrained by limitations. Existing methods often rely on single-modal data, restricting diagnostic comprehensiveness. Furthermore, approaches that utilize multi-modal data typically depend on simplistic temporal alignment,… More >
Open Access
ARTICLE
Yuanjing Luo1,2,#, Xichen Tan1,#, Yinuo Jiang1, Zhiping Cai1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077803
Abstract With increasing artwork plagiarism incidents, the necessity of using digital watermarking technology for high-quality artwork copyright protection is evident. Current digital watermarking methods are limited in imperceptibility and robustness. To address this, based on comprehensive copyright protection research, we develop a novel watermark framework named ArtFlow, using Invertible Neural Networks (INN). Our framework treats watermark embedding and recovery as inverse image transformations, implemented through forward and reverse processes of INN. To ensure high-quality watermark embedding, we utilize frequency domain transformations and attention mechanisms to guide the watermark into high-frequency areas of the image that have More >
Open Access
ARTICLE
Nader Karmous1, Leila Bousbia1, Mohamed Ould-Elhassen Aoueileyine1, Imen Filali2,*, Ridha Bouallegue1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077454
Abstract This study proposes an intelligent Intrusion Detection and Prevention System (IDPS) integrated into a centralized Ryu Software-Defined Networking (SDN) controller to mitigate replay attacks within Internet of Things (IoT) environments. To address the scarcity of specialized datasets, a comprehensive dataset was generated using a real-time SDN-IoT testbed encompassing Mininet, multiple OpenFlow 1.3 switches, and a single Ryu controller. The experimental setup featured the exchange of legitimate and malicious Message Queuing Telemetry Transport (MQTT) traffic between hosts and IoT devices to simulate realistic network behaviors and attack vectors. Our methodology introduces a novel feature engineering framework… More >
Open Access
ARTICLE
Mustafa Qaid Khamisi1, Zuping Zhang1,*, Mohammed Al-Habib1, Muhammad Asim2, Sajid Shah2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077556
Abstract Large Vision-Language models (VLMs) such as Contrastive Language-Image Pretraining (CLIP) have transformed open world image recognition. Nevertheless, few-shot classification, particularly in the extremely low-shot regime, requires not only high accuracy but also reliably calibrated uncertainty for decisions with high confidence. Existing training-free CLIP adapters are primarily designed to increase accuracy and efficiency; integrate the zero-shot text logits with the few-shot feature caches, but not definitely model predictive uncertainty and therefore often exhibit considerable miscalibration and weak selective performance. Bayesian adapters move in the direction of probabilistic modeling by placing priors over adapter parameters and employing… More >
Open Access
ARTICLE
Lijun Cui1, Yongjie Niu2, Yuxiang Sun1, Xiaokang Gu1, Jing Guo1, Pengfei Xu1,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079691
Abstract Indoor intrusion detection is essential for various applications, including security systems and smart homes. Recently, WiFi-based detection has gained popularity due to its low cost and non-invasive nature. Current Channel State Information (CSI) based frameworks primarily use deep learning to extract gait signatures; however, their performance depends heavily on extensive labeled datasets. These methods struggle to differentiate between unlabeled and labeled data that exhibit similar features. To address this challenge, we propose a novel Two-level Feature Fusion model for Indoor Intrusion Detection (TFF-IID) utilizing commercial WiFi CSI. The model adopts a two-level structure to learn… More >
Open Access
ARTICLE
Samer R. Sabbah1, Mohammad Rasmi Al-Mousa1, Ala’a Al-Shaikh2, Ahmad Al Smadi3,*, Suhaila Abuowaida4, Amina Salhi5,*, Arij Alfaidi6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079241
（This article belongs to the Special Issue: Next-Generation Cybersecurity: AI, Post-Quantum Cryptography, and Chaotic Innovations)
Abstract Web 4.0 platforms introduce intelligent, decentralized agents and real-time interactions that increase both utility and attack surface. This paper presents a comprehensive, reproducible AI blockchain hybrid designed to (1) detect SQL injection attacks at scale using a textual TFIDF + machine-learning pipeline, (2) incorporate reputation signals from a real-world Bitcoin OTC trust dataset to compute a TrustAlert Score (TAS) that prioritizes alerts and guides logging policy, and (3) record privacy-preserving audit digests on blockchain, optionally attested via a zero-knowledge proof (ZKP) pipeline. We evaluate the system on a 148 k SQL corpus and Soc-SignBitcoinOTC reputation More >
Open Access
ARTICLE
Xin Lu1, Jian-Hong Wang1,*, Kuo-Chun Hsu2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078547
（This article belongs to the Special Issue: Intelligent Personalized Recommender Systems: Deep Learning and Multimodal Approaches)
Abstract Aiming at the problems of data sparsity, uneven behavior weight allocation, and insufficient timeliness modeling existing in traditional recommendation systems in the scenario of personalized fashion recommendation, this paper proposes a personalized recommendation method that integrates multi-behavior weights and multi-modal features. A dynamic weighted collaborative filtering algorithm is designed, which comprehensively considers the multi-dimensional behaviors of users, and introduces a time attenuation factor to construct a time-sensitive user-item scoring matrix, so as to more accurately depict the dynamic changes of user interests. A multi-modal deep fusion framework is built: ResNet-50 is used to extract commodity… More >
Open Access
ARTICLE
Mengqi Liu, Haiqing Yao*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078181
（This article belongs to the Special Issue: Advances in Wireless Sensor Networks: Security, Efficiency, and Intelligence)
Abstract For the wide-coverage application scenarios, wireless rechargeable sensor networks are normally divided into multiple clusters to support the diversity and flexibility for monitoring, and use the mobile charger (MC) to support the sustainable charging of the network. Many efforts focus on optimizing the cluster head selection and mobile charger scheduling to improve the network energy efficiency and reliability. However, the existing work tends to use fixed triggering mechanism for cluster head (CH) rotation, and may trigger the rotation either too early or too late. Besides, the existing charging triggering mechanisms cannot track the changes in… More >
Open Access
ARTICLE
Xi Li1, Yingjie Chang1, Peng Chen1,*, Ang Bian1, Ning Lu1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077521
Abstract Multivariate time series anomaly detection (MTSAD) is a critical task for real-time risk control and fault diagnosis in industrial monitoring, aerospace, and financial domains. Unsupervised MTSAD confronts three core challenges: label scarcity in practical scenarios, diverse anomaly patterns that demand adaptive modeling, and weak feature discriminability between normal and anomalous samples. To address these challenges, we propose a Prototype Memory and Contrastive Learning Based Unsupervised Anomaly Detection for Multivariate Time Series method named PC-UAD. PC-UAD comprises three core modules with hierarchical functionalities: (1) A Temporal PatchEmbedder, which adopts learnable positional encoding for dynamic temporal representation… More >
Open Access
ARTICLE
Chan-Min Hsu1, Shang-Ru Yang1, Yi-Ju Lee1, An-Chi Wei1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076098
（This article belongs to the Special Issue: Advancements in Pattern Recognition through Machine Learning: Bridging Innovation and Application)
Abstract U-Net, a fully convolutional neural network (FCNN) with U-shaped features, has demonstrated significant success in biomedical image segmentation. However, the locality of convolution operations in the U-Net limits its ability to learn long-range dependencies. Transformers, originally developed for natural language processing, have recently been adapted for image segmentation because of their global self-attention mechanisms. Inspired by the long-range feature learning capability of transformers, we propose Dense-Transformer (DenT), an architecture designed for volumetric microscopy image segmentation. DenT incorporates transformers as encoders within each convolutional layer to capture global contextual information. Additionally, dense skip connections at multiple More >
Open Access
ARTICLE
Tianhua Lin1, Sijiang Xie1,*, Yalong Yan2, Jianguo Xie2, Ang Liu2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075573
Abstract Quantum key distribution (QKD) provides unconditional security but relies on repeaters to extend coverage, thereby introducing repeater trust risks—compromised repeaters may leak keys. Brakerski/Fan-Vercauteren scheme (BFV)-based QKD addresses this issue through key encryption and quantum attack resistance. However, Fast Fully Homomorphic Encryption over the Torus (TFHE) outperforms BFV in encryption/decryption speed for single-qubit homomorphic XOR operations, which is critical for the real-time requirements of QKD. We propose TFHE-based QKD (TQKD), a quantum key distribution protocol based on public-key TFHE. During key forwarding, it leverages the “usable-but-unobservable” property of homomorphic encryption to prevent key exposure. A… More >
Open Access
ARTICLE
Wajdan Al Malwi1,*, Fatima Asiri1, Muhammad Shahbaz Khan2,3,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.075326
（This article belongs to the Special Issue: Advances in Secure Computing: Post-Quantum Security, Multimedia Encryption, and Intelligent Threat Defence)
Abstract Modern intrusion detection systems (IDS) struggle to recognise zero-day cyberattacks, as classical discriminative models rely on historical attack labels and fail to characterise deviations from normal network behaviour. This work presents a hybrid quantum–classical intrusion detection framework in which a Quantum Circuit Born Machine (QCBM) models benign traffic as a probabilistic quantum state. The trained QCBM assigns each network flow a Quantum Anomaly Score (QAS), defined as the negative log-likelihood under the learned benign distribution, which is subsequently fused with classical flow statistics in a Light Gradient Boosted Machine (LightGBM) classifier. The proposed system employs a… More >
Open Access
REVIEW
Noor Mueen Mohammed Ali Hayder1,2, Seyed Amin Hosseini Seno2,*, Mehdi Ebady Manaa3,4, Hamid Noori2, Davood Zabihzadeh5
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078546
（This article belongs to the Special Issue: Advances in Machine Learning and Artificial Intelligence for Intrusion Detection Systems)
Abstract With the rapid expansion of networked systems, Distributed Denial-of-Service (DDoS) attacks have become a major threat to Internet security and service availability. Due to their limited scalability, incapacity to capture temporal and relational relationships, and decreased detection accuracy under dynamic and high-volume network traffic, traditional machine learning algorithms frequently fail in large-scale DDoS scenarios. This encourages the application of deep learning techniques that can simulate intricate relationships. This survey systematically reviews graph-based deep learning and Transformer models for DDoS detection. We categorize methods for transforming network traffic into graph representations and analyze key architectures, including… More >
Open Access
ARTICLE
Wanhui Qiao1, Xiaorui Zhang1,*, Wei Sun2, Shiyu Zhou3, Kaibo Wang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.077977
（This article belongs to the Special Issue: Advances in Image Recognition: Innovations, Applications, and Future Directions)
Abstract Ship re-identification (Re-ID) aims to match ship identities across disjoint camera views and separated time periods, which is critical for maritime target tracking and law enforcement. In real-world surveillance, variations in target distance and viewing angle frequently produce partial views and occlusions, leading to missing geometric components and fragmented appearance cues. Such incomplete observations substantially degrade the robustness and generalization of conventional single-frame methods that rely on global appearance representations. To address these challenges, this study proposes a new ship re-identification framework based on dual-stream feature decoupling and temporal variational Bayesian inference. The proposed method… More >
Open Access
ARTICLE
Jianing Zhang1, Zhikui Chen1,*, Jing Gao1, Peng Li2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.076717
（This article belongs to the Special Issue: Multimodal Learning for Big Data)
Abstract With the increasing development of ocean information technology, the multi-view fuzzy clustering is attracting increasing attention in pattern mining for massive multi-view ocean data of heterogeneous distributions, owing to its superior performance. However, the previous multi-view fuzzy clustering methods cannot fully consider informative topologies hidden in data distributions, which are crucial to recognize partitions of data. Moreover, they fail to capture invariant structures of multi-view ocean data in learning clustering-specific fusion representation. In addition, they do not take into consideration consistencies contained in the manifolds of data generation in mining soft patterns. To address those… More >
Open Access
ARTICLE
Linbin Li1, Ruiyuan Xue1,*, Juyin Zhang2,*, Xueping Wang2, Tiantian Chu1
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079732
（This article belongs to the Special Issue: Machine Learning in the Mechanics of Materials and Structures)
Abstract The plastic strain accumulation results of the multi-layered wrapped pressure vessel liner during long-term service are an important basis for its safety performance evaluation. However, the complex welds distributed on the liner bring challenges to the calculation of plastic cumulative strain. To this end, a novel hybrid deep learning framework is proposed for the efficient and precise prediction of ratcheting behavior in the liner welds of multilayered pressure vessels. By employing a BiLSTM network to extract bidirectional temporal dependencies from the strain history and incorporating a Multi-Head Attention (MHA) mechanism for adaptive feature weighting, the… More >
Open Access
ARTICLE
Kelan Wang1, Jianfei Chen2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079644
（This article belongs to the Special Issue: Attention Mechanism-based Complex System Pattern Intelligent Recognition and Accurate Prediction)
Abstract As 6G-enabled Industrial Internet of Things (IIoT) evolves, green and sustainable industrial monitoring increasingly relies on edge AI to deliver low-latency diagnosis under tight resource constraints. Industrial cyber–physical systems increasingly rely on heterogeneous sensing and communication infrastructures, where network-side attacks can propagate into physical processes and appear as coupled anomalies. Reliable diagnosis therefore requires joint learning from time-synchronized cyber and physical telemetry rather than modeling them as independent signals. This paper develops Cyber–Physical Symbiosis Network (CPSNet), a model designed for edge-AI deployment with a dual-stream architecture for fixed-window multiclass cross-domain anomaly diagnosis in IIoT. CPSNet… More >
Open Access
ARTICLE
Walaa Alayed1, Asad Ur Rehman2, M.Awais Ehsan3, Waqar Ul Hassan4, Ahmed Zeeshan5,6,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078078
（This article belongs to the Special Issue: Advances in Secure Computing: Post-Quantum Security, Multimedia Encryption, and Intelligent Threat Defence)
Abstract The rapid growth in the field of data and cloud computing has made it essential to ensure information security. Encryption consists of multiple layers, among which a critical component is the Substitution box (S-box). The S-box provides nonlinearity and confusion between the original and cipher forms, and its performance directly determines the security of the cipher against cryptanalysis. Chaotic systems have been widely used for image encryption, however, they suffer from well known limitations such as deterministic periodicity and reduced unpredictability in finite field digital environments. To address these issues, we propose a new S-box… More >
Open Access
ARTICLE
Qingyao Gu1,2,#, Mengqi Hu2,#, Zerui Zhao2, Liquan Chen2,*, Huiyu Fang2
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078873
Abstract With the rapid development of the Internet of Things (IoT), the widespread adoption of applications such as smart homes and industrial IoT has raised the demand for secure authentication and key agreement among resource-constrained devices over open communication channels. Traditional authentication protocols often rely on centralized servers for key distribution, which results in high communication overhead and exposes systems to single-point-of-failure risks. Moreover, IoT devices are typically constrained in computational resources and are vulnerable to hardware cloning. These limitations necessitate lightweight yet robust security mechanisms. To address these challenges, we propose a lightweight peer-to-peer authentication More >
Open Access
ARTICLE
Bing Xiong1, Zhuoxiong Li1, Yongqing Liu1, Yu Tang1, Jinyuan Zhao2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078464
Abstract Persistent Infrequent Flows (PIFs) refer to the packet flows that last for a long time but always at low frequencies in network traffic. Accurate identification of the PIFs plays a vital role in intrusion detection, attack prevention, traffic engineering, and other network fields. However, existing methods often require to save all flows for finding out the PIFs due to their infrequency feature, which brings about the problem of low identification accuracy and high memory overhead. To solve this problem, this paper proposes an accurate PIF identification method with low overhead called PIF-Identifier, composed of a… More >
Open Access
ARTICLE
Xingyun Hu1,2, Siqi Lu1,2,*, Liujia Cai1,2, Ye Feng1,2, Shuhao Gu1,2, Tao Hu1, Yongjuan Wang1,2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079484
（This article belongs to the Special Issue: Cyberspace Mapping and Anti-Mapping Techniques)
Abstract With the widespread adoption of web applications and cloud services, the OAuth 2.0-based OpenID Connect (OIDC) Single Sign-on (SSO) protocol has become the core of modern digital identity authentication. Although the OIDC protocol itself has strict security specifications, its implementation in real-world web frameworks can introduce critical vulnerabilities, particularly the improper omission of the state parameter, which leads to severe authentication forgery risks. Existing research often overlooks these implementation-level flaws, especially from a formal analysis perspective. This paper addresses this gap by formally analyzing the authentication forgery attack resulting from the missing state parameter. We construct… More >
Open Access
ARTICLE
Abdullah Alshammari*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078634
（This article belongs to the Special Issue: Advances in Cybersecurity for Digital Ecosystems)
Abstract The proliferation of Internet of Things (IoT) devices in the infrastructure of smart cities has posed cybersecurity risks like never before, which have direct implications on the sustainability and energy consumption of cities. In this paper, a multi-faceted Threat-Resilient Energy-Conscious Security Framework (TRECSF) is introduced that combines intrusion detection methods powered by deep learning, blockchain-driven data integrity verification mechanism, and energy-aware security protocols in smart city ecosystems to achieve their sustainability. The new Hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model is introduced to the proposed architecture, which fulfills the purpose of the study to… More >
Open Access
ARTICLE
Xiaoping Yang1,*, Songjie Yang2, Junqi Long1, Quanzeng Wang3, Bin Yang4, Xiaofang Cao5, Guochao Qi6
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078105
（This article belongs to the Special Issue: Advances in Wireless Sensor Networks: Security, Efficiency, and Intelligence)
Abstract The rapid proliferation of latency-sensitive applications, coupled with the limitations of service range, has driven the integration of aerial simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASTAR-RIS) and task offloading to enhance both communication and computational efficiency in wireless sensor networks (WSNs). However, in WSNs, conventional ASTAR-RIS-assisted task offloading faces critical limitations, including restricted endurance, underutilized network caching and computing resources, and inefficient resource allocation within the optimization framework. To overcome these challenges, this paper integrates wireless power transfer (WPT) technology and proposes a novel energy-efficient ASTAR-RIS and WPT-assisted task offloading and content caching framework… More >
Open Access
ARTICLE
Quang Dong Nguyen Vo1, Gia Nhu Nguyen1, Hoang Vu Tran2,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.078756
Abstract Reliable vehicle detection in urban traffic environments remains challenging, particularly for fixed-view CCTV systems deployed in Southeast Asian cities, where heterogeneous traffic composition, high traffic density, frequent occlusions, and complex visual conditions are prevalent. The absence of large-scale datasets tailored to such mixed-traffic environments poses a significant limitation to the performance and generalization capability of existing object detection models. To address this gap, this paper presents a large-scale traffic image dataset for real-time vehicle detection in Vietnamese urban environments. The proposed dataset comprises 23,364 images collected from fixed-view CCTV traffic cameras deployed across Da Nang… More >
Open Access
ARTICLE
Sara H. Elsayed1, Rodaina Abdelsalam1, Mahmoud A. Ismail Shoman2, Raed Alotaibi3,*, Omar Reyad4,5,*
CMC-Computers, Materials & Continua, DOI:10.32604/cmc.2026.079018
（This article belongs to the Special Issue: Security and Privacy in IoT: Cross-Domain Approaches and Cryptographic Innovations)
Abstract In today’s digitally connected world, where cyber threats are becoming increasingly complex, finding modern and secure text encryption solutions that maintain maximum runtime performance while offering high-level protection is more crucial. The deployment of sophisticated security paradigms is often accompanied by a significant escalation in computational overhead. Thus, the fundamental objective resides in the mitigation of computational overhead while maintaining an uncompromising security posture. Internet of Things (IoT) devices require strong security measures for data transmission. Also, protecting communication channels against illegal access and eavesdropping has become crucial due to the exponential expansion of the… More >
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