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Semi-supervised range-based anomaly detection for cloud systems
Synechron Technologies Pvt. Ltd, Pune, Maharastra, India.
Ernst and Young Global LLP, Gurugram, India.
Umeå University, Faculty of Science and Technology, Department of Computing Science.
Umeå University, Faculty of Science and Technology, Department of Computing Science.ORCID iD: 0000-0002-2633-6798
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2023 (English)In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 20, no 2, p. 1290-1304Article in journal (Refereed) Published
Abstract [en]

The inherent characteristics of cloud systems often lead to anomalies, which pose challenges for high availability, reliability, and high performance. Detecting anomalies in cloud key performance indicators (KPI) is a critical step towards building a secure and trustworthy system with early mitigation features. This work is motivated by (i) the efficacy of recent reconstruction-based anomaly detection (AD), (ii) the misrepresentation of the accuracy of time series anomaly detection because point-based Precision and Recall are used to evaluate the efficacy for range-based anomalies, and (iii) detects performance and security anomalies when distributions shift and overlaps. In this paper, we propose a novel semi-supervised dynamic density-based detection rule that uses the reconstruction error vectors in order to detect anomalies. We use long short-term memory networks based on encoder-decoder (LSTM-ED) architecture to reconstruct the normal KPI time series. We experiment with both testbed and a diverse set of real-world datasets. The experimental results show that the dynamic density approach exhibits better performance compared to other detection rules using both standard and range-based evaluation metrics. We also compare the performance of our approach with state-of-the-art methods, outperforms in detecting both performance and security anomalies.

Place, publisher, year, edition, pages
IEEE, 2023. Vol. 20, no 2, p. 1290-1304
Keywords [en]
Anomaly detection, Cloud reliability, Dynamic density, LSTM encoder-decoder, Range-based evaluation metrics, Time series reconstruction
National Category
Computer Sciences Computer Systems
Identifiers
URN: urn:nbn:se:umu:diva-202020DOI: 10.1109/TNSM.2022.3225753Scopus ID: 2-s2.0-85144018247OAI: oai:DiVA.org:umu-202020DiVA, id: diva2:1722464
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Available from: 2022-12-29 Created: 2022-12-29 Last updated: 2023-07-14Bibliographically approved

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Bhutto, Adil BinElmroth, ErikBhuyan, Monowar H.

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CiteExportLink to record
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  • apa
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