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Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model

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Big Data (BigData 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1496))

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Abstract

Novelty detection (ND) is a crucial task in machine learning to identify anomalies in the test data in some respects different from the training data. As an anomaly detection method, novelty detection only uses normal samples for model learning, which can well fit most of the natural scenes that the amount of abnormal samples is in fact strongly insufficient, such as network intrusion detection, industrial fault detection, and so on, due to the rareness of abnormal events or the high cost of abnormal samples collection. This paper proposes a reconstruction-based ND scheme by introducing an optimized deep generative model (ODGM), which combines the concept of Variational Auto-encoder (VAE) and the generative adversarial network (GAN) model jointly to efficiently and stably learn the essential characteristics from normal samples. A novelty index is established by combining signal reconstruction loss and feature loss between the original signal of the reconstructed signal based on the ODGM on normal samples for anomaly point identification in the test data. The effectiveness and superiority of the proposed model is validated and compared with other representative deep learning-based novelty detection models on two public data sets.

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Notes

  1. 1.

    http://yann.lecun.com/exdb/mnist/.

  2. 2.

    https://www.unb.ca/cic/datasets/nsl.html.

  3. 3.

    https://www.unb.ca/cic/datasets/nsl.html.

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Acknowledgements

This work was supported by the national natural science foundation of China under grant No.61971188 and 61771492, in part by the Scientific Research Project of Hunan Education Department under grant No.19B364.

Author information

Authors and Affiliations

  1. Hunan Meteorological Science Institute, Changsha, 410007, Hunan, China

    Lianye Liu

  2. Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, Changsha, 410081, Hunan, China

    Jinping Liu, Juanjuan Wu, Jiaming Zhou & Meiling Cai

Authors
  1. Lianye Liu
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  2. Jinping Liu
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  3. Juanjuan Wu
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  4. Jiaming Zhou
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  5. Meiling Cai
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Editor information

Editors and Affiliations

  1. National University of Defense Technology, Changsha, China

    Xiangke Liao

  2. Shenzhen University of Technology, Chinese Academy of Sciences, Shenzhen, China

    Wei Zhao

  3. University of Science and Technology of China, Hefei, China

    Enhong Chen

  4. Sun Yat-sen University, Guangzhou, China

    Nong Xiao

  5. Taiyuan University of Technology, Taiyuan, China

    Li Wang

  6. Nanjing University, Nanjing, China

    Yang Gao

  7. Nanjing University, Nanjing, China

    Yinghuan Shi

  8. Sun Yat-sen University, Guangzhou, China

    Changdong Wang

  9. Sun Yat-sen University, Guangzhou, China

    Dan Huang

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Liu, L., Liu, J., Wu, J., Zhou, J., Cai, M. (2022). Novelty Detection-Based Automated Anomaly Identification via Optimized Deep Generative Model. In: Liao, X., et al. Big Data. BigData 2021. Communications in Computer and Information Science, vol 1496. Springer, Singapore. https://doi.org/10.1007/978-981-16-9709-8_9

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  • DOI: https://doi.org/10.1007/978-981-16-9709-8_9

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  • Online ISBN: 978-981-16-9709-8

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