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Enhancing Time Series Anomaly Detection: A Knowledge Distillation Approach with Image Transformation. [PDF]
Sensors (Basel)Anomaly detection is critical in safety-sensitive fields, but faces challenges from scarce abnormal data and costly expert labeling. Time series anomaly detection is relatively challenging due to its reliance on sequential data, which imposes high ...
Park H, Jang H.
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CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting. [PDF]
Sci DataAnomaly detection in network traffic is crucial for maintaining the security of computer networks and identifying malicious activities. Most approaches to anomaly detection use methods based on forecasting.
Koumar J, Hynek K, Čejka T, Šiška P.
europepmc +2 more sources
Generative adversarial synthetic neighbors-based unsupervised anomaly detection. [PDF]
Sci RepAnomaly detection is crucial for the stable operation of mechanical systems, securing financial transactions, and ensuring network security, among other critical areas.
Chen L +6 more
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Research Progress of Video Anomaly Detection Technology [PDF]
Jisuanji kexue yu tansuo, 2022 Video anomaly detection refers to the detection and identification of events that deviate from normal behavior, which has a wide range of applications in surveillance video.
WU Kaijun, HUANG Tao, WANG Dicong, BAI Chenshuai, TAO Xiaomiao
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Multi-Perspective Anomaly Detection [PDF]
Sensors, 2021 Anomaly detection is a critical problem in the manufacturing industry. In many applications, images of objects to be analyzed are captured from multiple perspectives which can be exploited to improve the robustness of anomaly detection. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly ...
Peter Jakob +3 more
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SA-PatchCore: Anomaly Detection in Dataset With Co-Occurrence Relationships Using Self-Attention
IEEE Access, 2023 Various unsupervised anomaly detection methods using deep learning have recently been proposed, and the accuracy of the anomaly detection technique for local anomalies has been improved.
Kengo Ishida +5 more
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G2D: Generate to Detect Anomaly [PDF]
2021 IEEE Winter Conference on Applications of Computer Vision (WACV), 2021 In this paper, we propose a novel method for irregularity detection. Previous researches solve this problem as a One-Class Classification (OCC) task where they train a reference model on all of the available samples. Then, they consider a test sample as an anomaly if it has a diversion from the reference model.
Pourreza, Masoud +5 more
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Anomaly Detection As-a-Service [PDF]
2019 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW), 2019 Paper accepted at the Intl. Workshop on Governing Adaptive and Unplanned Systems of Systems (GAUSS)
Mobilio, M +4 more
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Improvement in detection of presence in forbidden locations in video anomaly using optical flow map [PDF]
هوش محاسباتی در مهندسی برق, 2023 Anomaly detection has been in researchers’ scope of study for a long time. The wide variety of anomaly detection use cases ranges from quality control in production lines to providing security in public places.
Mohammad Rahimpour +3 more
doaj +1 more source
Detecting network performance anomalies with contextual anomaly detection [PDF]
2017 IEEE International Workshop on Measurement and Networking (M&N), 2017 Network performance anomalies can be defined as abnormal and significant variations in a network's traffic levels. Being able to detect anomalies is critical for both network operators and end users. However, the accurate detection without raising false alarms can become a challenging task when there is high variance in the traffic.
Dimopoulos, Giorgos +3 more
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