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Anomaly Detection As-a-Service [PDF]

open access: yes2019 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
openaire   +3 more sources

Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [PDF]

open access: yesIEEE Transactions on Neural Networks and Learning Systems, 2021
Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems.
Yixin Liu   +5 more
semanticscholar   +1 more source

Detecting network performance anomalies with contextual anomaly detection [PDF]

open access: yes2017 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
openaire   +3 more sources

VT-ADL: A Vision Transformer Network for Image Anomaly Detection and Localization [PDF]

open access: yesInternational Symposium on Industrial Electronics, 2021
We present a transformer-based image anomaly detection and localization network. Our proposed model is a combination of a reconstruction-based approach and patch embedding.
P. Mishra   +4 more
semanticscholar   +1 more source

DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection [PDF]

open access: yesKnowledge Discovery and Data Mining, 2023
Time series anomaly detection is critical for a wide range of applications. It aims to identify deviant samples from the normal sample distribution in time series. The most fundamental challenge for this task is to learn a representation map that enables
Yiyuan Yang   +4 more
semanticscholar   +1 more source

Subspace-Based Anomaly Detection for Large-Scale Campus Network Traffic

open access: yesJournal of Applied Mathematics, 2023
With the continuous development of information technology and the continuous progress of traffic bandwidth, the types and methods of network attacks have become more complex, posing a great threat to the large-scale campus network environment.
Xiaofeng Zhao, Qiubing Wu
doaj   +1 more source

Improvement in detection of presence in forbidden locations in video anomaly using optical flow map [PDF]

open access: yesهوش 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

Saliencycut: Augmenting Plausible Anomalies for Anomaly Detection

open access: yesPattern Recognition, 2023
Anomaly detection under open-set scenario is a challenging task that requires learning discriminative fine-grained features to detect anomalies that were even unseen during training. As a cheap yet effective approach, data augmentation has been widely used to create pseudo anomalies for better training of such models.
Jianan Ye   +5 more
openaire   +2 more sources

Network anomaly detection: a survey and comparative analysis of stochastic and deterministic methods [PDF]

open access: yes, 2013
7 pages. 1 more figure than final CDC 2013 versionWe present five methods to the problem of network anomaly detection. These methods cover most of the common techniques in the anomaly detection field, including Statistical Hypothesis Tests (SHT), Support
Cassandras, C. G.   +3 more
core   +8 more sources

Detecting Semantic Anomalies

open access: yesProceedings of the AAAI Conference on Artificial Intelligence, 2020
We critically appraise the recent interest in out-of-distribution (OOD) detection and question the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we argue that out-distributions of practical interest are ones where the distinction is semantic in nature for a specified context ...
Faruk Ahmed, Aaron Courville
openaire   +4 more sources

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