Mahalanobis Distance based Adversarial Network For Anomaly Detection

Mahalanobis Distance based Adversarial Network For Anomaly Detection
Title:
Mahalanobis Distance based Adversarial Network For Anomaly Detection
Other Titles:
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Publication Date:
09 April 2020
Citation:
Y. Hou, Z. Chen, M. Wu, C. Foo, X. Li and R. M. Shubair, "Mahalanobis Distance Based Adversarial Network for Anomaly Detection," ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 3192-3196, doi: 10.1109/ICASSP40776.2020.9053206.
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.
License type:
PublisherCopyrights
Funding Info:
A*STAR IIoT Research Program under the RIE2020 IAF-PP Grant A1788a0023.
Description:
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2379-190X
1520-6149
ISBN:
978-1-5090-6631-5
978-1-5090-6632-2
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