Trustworthy Anomaly Detection: A Survey [PDF]
arXiv, 2022 Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting various anomalies.
arxiv
Distributed Anomaly Detection in Smart Grids: A Federated Learning-Based Approach
IEEE Access, 2023 The smart grid integrates Information and Communication Technologies (ICT) into the traditional power grid to manage the generation, distribution, and consumption of electrical energy. Despite its many advantages, it faces significant challenges, such as
J. Jithish +3 more
semanticscholar +1 more source
Model selection for anomaly detection [PDF]
SPIE Proceedings, 2015 6 pages, 3 figures, Eighth International Conference on Machine Vision (December 8, 2015)
Dmitry Smolyakov +2 more
openaire +4 more sources
An Adaptive Policy-Based Anomaly Object Control System for Enhanced Cybersecurity
IEEE AccessAnomaly detection research focuses on identifying rare patterns derived from daily occurrences. This study introduces an innovative anomaly–object control system that utilizes adaptive policies through anomaly detection algorithms.
Won Sakong, Wooju Kim
doaj +1 more source
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]
PLoS ONE, 2016 Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
doaj +1 more source
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detection [PDF]
arXiv, 2021 Visual surface anomaly detection aims to detect local image regions that significantly deviate from normal appearance. Recent surface anomaly detection methods rely on generative models to accurately reconstruct the normal areas and to fail on anomalies.
arxiv
Hyperspectral Anomaly Detection Method Based on Low Rank Total Variation Regu-larization
Jisuanji kexue yu tansuo, 2020 Hyperspectral remote sensing technology provides abundant spectral information for exploring objects and supplies a better data source for anomaly detection.
XU Chao, ZHAN Tianming
doaj +1 more source
Machine learning for Internet of things anomaly detection under low-quality data
International Journal of Distributed Sensor Networks, 2022 With the popularization of Internet of things, its network security has aroused widespread concern. Anomaly detection is one of the important technologies to protect network security.
Shangbin Han, Qianhong Wu, Yang Yang
doaj +1 more source
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection [PDF]
arXiv, 2023 Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e., samples from open-set anomaly classes), while effectively identifying the seen anomalies.
arxiv
A Comparative Study of Time Series Anomaly Detection Models for Industrial Control Systems
Italian National Conference on Sensors, 2023 Anomaly detection has been known as an effective technique to detect faults or cyber-attacks in industrial control systems (ICS). Therefore, many anomaly detection models have been proposed for ICS.
Bedeuro Kim +5 more
semanticscholar +1 more source