Collective Anomaly Detection using Generative Adversarial Networks

DOI

Bibliographic Information

Other Title
  • 敵対的生成ネットワークを用いた集団型異常検知

Description

<p>Generative adversarial network (GAN) is now being applied to anomaly detection. However, the existing approaches to GAN-based anomaly detection cannot detect collective anomalies that change the behavior of some data instances because they deal with individual data instances. This study aims to determine how collective anomalies that are commonly associated with time-series data can be detected using GAN models. We developed a GAN model for time-series data by adopting a decoder side of sequence to sequence (seq2seq) to a generator, an encoder side of seq2seq to an encoder, recurrent neural networks and fully connected neural network to a discriminator. We conducted several experiments on datasets, regarded as anomaly datasets, that we generated by swapping data instances at different time points. The results suggest that our GAN model can compete effectively with existing approaches for detecting collective anomalies.</p>

Journal

Keywords

Details 詳細情報について

  • CRID
    1390001288141814016
  • NII Article ID
    130007658691
  • DOI
    10.11517/pjsai.jsai2019.0_4a2j302
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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