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.
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.
A*STAR IIoT Research Program under the RIE2020 IAF-PP Grant A1788a0023.