Least Squares Generative Adversarial Networks-Based Anomaly Detection

Abstract

Multivariate statistical process control (MSPC) is a technique for detecting anomalies by monitoring several quality characteristics simultaneously. For the MSPC problem, the Hotelling’s $T^{2}$ control chart has been widely used as a typical method. Recently, researchers have converted the MSPC problem into a classification problem such as the artificial contrast (AC) and the one-class classification (OCC). Previous studies have shown that these methods outperform the Hotelling’s $T^{2}$ chart when the data do not follow a multivariate normal distribution. However, unless the size of the process data is enough for the AC and the OCC, they cannot work properly. To tackle this problem, in this paper, we propose a novel anomaly detection (AD) approach. The proposed method adopts the least square generative adversarial network (LS-GAN) to estimate the probability distribution of the training data. It generates new training samples from the learned probability distribution. The classifiers such as the random forests (RF) and the one-class support vector machines (OC-SVM) are considered for tackling the AC and the OCC respectively. The numerical experiments demonstrate that the proposed approach outperforms the existing methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC).

Keywords

• Anomaly detection
• artificial contrast
• one-class classification
• least square generative adversarial network
• Hotelling’s control boundary
• random forests