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Deep Learning for Anomaly Detection

Published: 20 August 2020 Publication History

Abstract

Anomaly detection has been widely studied and used in diverse applications. Building an effective anomaly detection system requires researchers and developers to learn complex structure from noisy data, identify dynamic anomaly patterns, and detect anomalies with limited labels. Recent advancements in deep learning techniques have greatly improved anomaly detection performance, in comparison with classical approaches, and have extended anomaly detection to a wide variety of applications. This tutorial will help the audience gain a comprehensive understanding of deep learning based anomaly detection techniques in various application domains. First, we give an overview of the anomaly detection problem, introducing the approaches taken before the deep model era and listing out the challenges they faced. Then we survey the state-of-the-art deep learning models that range from building block neural network structures such as MLP, CNN, and LSTM, to more complex structures such as autoencoder, generative models (VAE, GAN, Flow-based models), to deep one-class detection models, etc. In addition, we illustrate how techniques such as transfer learning and reinforcement learning can help amend the label sparsity issue in anomaly detection problems and how to collect and make the best use of user labels in practice. Second to last, we discuss real world use cases coming from and outside LinkedIn. The tutorial concludes with a discussion of future trends.

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  • (2024)Label-Free Multivariate Time Series Anomaly DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3349613(1-15)Online publication date: 2024
  • (2024)EGNN-AD: An Effective Graph Neural Network-Based Approach for Anomaly Detection on Edge-Attributed GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_21(321-331)Online publication date: 31-Aug-2024
  • (2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
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cover image ACM Conferences
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
August 2020
3664 pages
ISBN:9781450379984
DOI:10.1145/3394486
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: 20 August 2020

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  1. anomaly detection
  2. deep learning

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View all
  • (2024)Label-Free Multivariate Time Series Anomaly DetectionIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.3349613(1-15)Online publication date: 2024
  • (2024)EGNN-AD: An Effective Graph Neural Network-Based Approach for Anomaly Detection on Edge-Attributed GraphsDatabase Systems for Advanced Applications10.1007/978-981-97-5572-1_21(321-331)Online publication date: 31-Aug-2024
  • (2023)MFGAD-INT: in-band network telemetry data-driven anomaly detection using multi-feature fusion graph deep learningJournal of Cloud Computing10.1186/s13677-023-00492-w12:1Online publication date: 28-Aug-2023
  • (2022)IOTA-Based Mobile Crowd SensingWireless Communications & Mobile Computing10.1155/2022/62741142022Online publication date: 1-Jan-2022
  • (2022)A Review of Neural Networks for Anomaly DetectionIEEE Access10.1109/ACCESS.2022.321600710(112342-112367)Online publication date: 2022

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