Ravi Saharan, Akrisht Singh, Prakash Choudhary^*

Computer Systems Science and Engineering, Vol.50, pp. 1-20, 2026, DOI:10.32604/csse.2026.081278 - 21 September 2026

Abstract The task of monitoring crowds for safety is a critical challenge, yet traditional surveillance systems are often visual only, error-prone, and lack interpretability. This paper presents SAFE (Semantic Audio-Visual Fusion Engine), a real-time, multi-modal framework that delivers interpretable, operator-facing alerts by fusing complementary audio-visual cues. The visual pipeline couples SSD–ResNet face detection and Hungarian tracking with facial emotion recognition and DBSCAN-based clustering of negative affect to compute a semantic visual anomaly score that includes an explicit overcrowding signal. In parallel, the audio pipeline extracts MFCCs (Mel-Frequency Cepstral Coefficients) and employs a lightweight one-dimensional convolutional neural… More >

Hui Xu, Shuang Qu^*, Pan Hu

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.087054 - 15 September 2026

Abstract With the rapid growth in the number of end devices in the Internet of Things (IoT), network traffic has become increasingly complex and redundant, while multiple attack types often coexist, posing major challenges to traffic anomaly detection. Traditional machine learning-based methods for IoT traffic anomaly detection often suffer from severe feature redundancy, high computational complexity, and low detection efficiency, making it difficult to simultaneously achieve high detection accuracy and computational efficiency. To address this issue, metaheuristic algorithms are often introduced in the feature selection stage to reduce feature redundancy and improve detection efficiency. However, the… More >

Gobinda Bhattacharjee¹, Joy Dey¹, Tanjim Mahmud^1,*, Mohammad Shahadat Hossain^2,3, Karl Andersson³

CMC-Computers, Materials & Continua, Vol.89, No.2, 2026, DOI:10.32604/cmc.2026.085032 - 15 September 2026

Abstract Log parsing is a fundamental prerequisite for automated system monitoring, anomaly detection, and root cause analysis in large-scale distributed environments. However, existing parsing approaches often rely on heuristic rules, manually engineered features, or fixed similarity thresholds, limiting their adaptability to heterogeneous and evolving log structures. To address these challenges, this study presents SemBERT, a fully unsupervised log parsing framework that integrates semantic BERT embeddings, Incremental Principal Component Analysis (IPCA), HDBSCAN clustering, and adaptive centroid-based cluster merging for robust template mining. Unlike conventional methods that employ fixed merging criteria, SemBERT adaptively determines semantic merging thresholds according… 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 >

Ifeanyi C. Emeto^*, Adamu A. Galadima, Ikechukwu H. Ezeh, Emmanuel O. Atomatofa, Christiana A. Okoloegbo

Journal of Cyber Security, Vol.8, pp. 609-640, 2026, DOI:10.32604/jcs.2026.084424 - 14 September 2026

Abstract Software-Defined Networking (SDN) introduces centralised network control and programmability, but its centralised control plane creates significant security vulnerabilities that can be exploited by cyber attackers. This study proposes and evaluates a hybrid security framework that integrates machine learning (ML)-based anomaly detection with blockchain-based authentication to enhance the security of the SDN control plane. The study aimed to analyse vulnerabilities in SDN architectures, develop an ML-driven attack detection model, secure API communications using blockchain-based authentication, and evaluate the performance and scalability of the proposed framework. Vulnerability assessment was conducted using Nmap and STRIDE threat modelling, while… More >

Chirag Devendrakumar Parikh^*

Journal on Big Data, Vol.8, pp. 11-26, 2026, DOI:10.32604/jbd.2026.077330 - 07 September 2026

Abstract Sensors and large-scale measurement systems generate continuous data streams used in industrial monitoring, IoT analytics, and decision-making. However, sensor drift, undocumented maintenance, environmental stress, and component variability often degrade the integrity and reliability of collected measurements. This study proposes a compliance-integrated data integrity framework that combines hardware qualification records, traceability documentation, lifecycle validation checkpoints, and automated anomaly detection methods. The framework introduces five integrity layers that link sensor characterization with statistical filtering and compliance-driven verification. To validate feasibility, a proof-of-concept simulation was conducted using drift-injected sensor datasets. Results show that integrating compliance metadata improve anomaly More >

Asim Niaz^1,#, Muhammad Umraiz^2,#, Syed Farhan Alam Zaidi³, Kwang Nam Choi^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.148, No.2, 2026, DOI:10.32604/cmes.2026.085102 - 28 August 2026

Abstract Automated visual inspection in industrial settings often struggles with limited defect data and poor generalization to unseen anomalies. To overcome this challenge, we propose a hybrid anomaly detection pipeline, which integrates embedding-based, reconstruction-based, and self-supervised learning approaches. The framework also proposes a new Realistic Industrial Defect Synthesis (RIDS) module that synthesizes structured and textured synthetic anomalies based on the target masks, composite maps, and blending techniques. This helps to learn from pseudo-labeled data without the need for large annotated datasets. The pipeline further includes ViLoc-Net, a Vision Transformer-based localization network that obtains global features and More >

Jing Cui¹, Yong Qin^2,*, Yixuan Geng³, Miao Guo^4,*, Xue Yang⁴, Wanyin Shi⁵

Structural Durability & Health Monitoring, Vol.20, No.5, 2026, DOI:10.32604/sdhm.2026.081306 - 24 August 2026

Abstract Noise barriers (NBs) play a significant role in reducing railway noise and preventing foreign-object intrusion. However, surface damage, corrosion, rust, missing components, and local deformation may gradually reduce their structural reliability and threaten railway operation safety. Because NB anomalies are diverse and defect samples are limited, it remains difficult to build a general detector using conventional supervised learning. To address this problem, this study proposes an unsupervised anomaly detection system for railway NBs using UAV imagery. First, a color-prior-based NB localization algorithm is developed in the HSV color space to extract NB regions without cumbersome More >

Zehan Li¹, Yingyi Li^2,*, Zhiwei Tang³, Xuemeng Zhai³, Jiandong Liang¹, Guangmin Hu³

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.086118 - 13 August 2026

Abstract Social network platforms have become primary channels for information dissemination, yet they are increasingly exploited by anomalous users such as bots, fake accounts, and coordinated disinformation spreaders. These malicious actors manipulate public opinion, spread misinformation and undermine platform integrity, posing severe threats to the security of the online ecosystem. Accurate detection of such users is challenging because they often organize into sophisticated high-order connection patterns that extend beyond local neighborhoods. Existing methods address this by either injecting predefined motifs as handcrafted features, which lack flexibility to discover unknown patterns, or employing higher-order Graph neural networks… More >

Dong-Fang Wu^1,2, Jiaojiao Deng^1,2, Zhiwei Ye^1,2,*, Rong Gao^1,2, Fan Ma^1,2, Dingfeng Song^1,2

CMC-Computers, Materials & Continua, Vol.89, No.1, 2026, DOI:10.32604/cmc.2026.085308 - 13 August 2026

Abstract Anomaly detection is used to identify data points deviating from normal patterns and plays a significant role in fields such as fault diagnosis, biomedicine, and cybersecurity. However, anomaly detection tasks in practical applications often involve high-dimensional data which presents challenges such as severe feature redundancy, hidden anomaly patterns, and difficulty in capturing uncertainty. These issues make it difficult to effectively identify anomalies, thereby limiting the model’s discriminative power and stability. To address these challenges, we propose a high-dimensional anomaly detection model, MSFO-EIF, integrating multi-strategy feature optimization with rough set modeling. First, in the feature space… More >