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Deep Learning for Anomaly Detection

Published: 22 January 2020 Publication History

Abstract

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires the researchers/developers to learn the complex structure from noisy data, identify the dynamic anomaly patterns and detect anomalies while lacking sufficient labels. Recent advancement in deep learning techniques has made it possible to largely improve anomaly detection performance compared to the classical approaches. This tutorial will help the audience gain a comprehensive understanding of deep learning-based anomaly detection techniques in various application domains. First, it introduces what is the anomaly detection problem, the approaches taken before the deep model era and the challenges it faced. Then it surveys the state-of-the-art deep learning models extensively and discusses the techniques used to overcome the limitations from traditional algorithms. Second to last, it studies deep model anomaly detection techniques in real world examples from LinkedIn production systems. The tutorial concludes with a discussion of future trends.

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cover image ACM Conferences
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining
January 2020
950 pages
ISBN:9781450368223
DOI:10.1145/3336191
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 22 January 2020

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  1. anomaly detection
  2. deep learning

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  • (2024)Semantic Hierarchical Classification Applied to Anomaly Detection Using System Logs with a BERT ModelApplied Sciences10.3390/app1413538814:13(5388)Online publication date: 21-Jun-2024
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