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Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains...

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of uns...

Abstract The Fermi surface provides indispensable insights into the electronic structure of materials. Here, we present a robust analysis framework employing an interpretable machine learning approach to investigate Ferm...

Unsupervised anomaly detection identifying both structural and logical anomalies is essential for modern industrial manufacturing. Although multi-score frameworks have been proposed, existing methods tend to reuse identi...

Abstract The increasing adoption of Industrial Internet of Things (IIoT) devices introduces significant cybersecurity and privacy challenges, particularly anomaly detection and secure data sharing. This study presents Se...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...

Abstract To address the challenge of abnormal tobacco shred detection in cigarette production lines, this study proposes an improved lightweight YOLOv8-based model that balances model efficiency and detection accuracy. F...

ABSTRACT Intelligent and adaptive defence systems that can quickly thwart changing cyberthreats are becoming more and more necessary in the dynamic and data‐intensive Internet of things (IoT) environment. Using the NSL...

As smart meter technology advances, advanced metering infrastructure (AMI) enables the collection of high-resolution energy consumption data, such as 15-minute interval electricity and gas usage. While anomaly detection ...

Abstract This paper proposes a novel hybrid ensemble machine learning framework integrated with Explainable AI (XAI) to overcome the fundamental trade-off between non-detection zones (NDZ) and false positives in conventi...

Abstract The rising complexity of urban areas, along with growing public safety needs, has made intelligent video surveillance systems more essential. The current surveillance systems that rely on human observation and p...

Plasmonic sensors exhibit tremendous potential to accomplish real-time, label-free, and high-sensitivity biosensing. Gold nanohole array (GNA) is one of the classic plasmonic nanostructures that can be readily fabricated...

This study investigates how algorithmic infrastructures on Chinese social media govern visibility not through post-hoc deletion but through pre-emptive design. Focusing on Xiaohongshu during the 2025 China—U.S. trade c...

Fault Detection and Diagnosis (FDD) is crucial for ensuring safe, reliable, and energy-efficient operation of collaborative robots, especially with the growth of Industry 4.0. Industrial robots are nonlinear, complex, an...

Industry 4.0 has brought numerous advantages, such as increasing productivity through automation. However, it also presents major cybersecurity issues, such as cyberattacks affecting industrial processes. Federated Learn...

In this article, we revisit and expand our prior work on graph similarity. As with our earlier work, we focus on a view of similarity which does not require node correspondence between graphs under comparison. Our work i...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

Under adverse weather conditions, ice accumulation and strong winds cause low-frequency and high-amplitude swaying of transmission lines, increasing the risk of wire fatigue, hardware damage, and tower collapse, posing a...

To address the sensitivity of traditional Two-Dimensional Principal Component Analysis (2DPCA) to outliers and the breakdown of variance-reconstruction equivalence under noise, we propose an Angle-Aware Weighted 2DPCA (A...