Mahalanobis Distance Based Adversarial Network for Anomaly Detection

Yubo Hou; Zhenghua Chen; Min Wu; Chuan-Sheng Foo; Xiaoli Li; Raed M. Shubair

ICASSP 2020accepted0 citations

Mahalanobis Distance Based Adversarial Network for Anomaly Detection

Yubo Hou, Zhenghua Chen, Min Wu, Chuan-Sheng Foo, Xiaoli Li, Raed M. Shubair

Abstract

Anomaly detection techniques are very crucial in multiple business applications, such as cyber security, manufacturing and finance. However, developing anomaly detection methods for high-dimensional data with high speed and good performance is still a challenge. Generative Adversarial Networks (GANs) are able to model the complex high-dimensional data, but they still require large computation in inference stage. This paper proposes an efficient method, known as Mahalanobis Distance-based Adversarial Network (MDAN), for anomaly detection. The proposed MDAN models the data using generative adversarial network (GAN) and detects anomalies by using the Mahalanobis distance. The proposed MDAN outperforms conventional GAN-based methods considerably and has a higher inference speed, when applied to several tabular and image datasets.

BibTeX

@inproceedings{icassp2020_mahalanobisdista,
  title = {Mahalanobis Distance Based Adversarial Network for Anomaly Detection},
  author = {Yubo Hou and Zhenghua Chen and Min Wu and Chuan-Sheng Foo and Xiaoli Li and Raed M. Shubair},
  booktitle = {ICASSP 2020},
  year = {2020}
}