Design and Development of AD CGAN: Conditional Generative Adversarial Networks for Anomaly Detection
Whether in the realm of software or hardware, datasets representing the state of systems are mostly imbalanced. This imbalance is because these systems' reliability requirements make the occurrence of an anomaly a rare phenomenon. Hence, most datasets on anomaly detection have a relatively small percentage that captures the anomaly. Recently, generative adversarial networks (GAN) have shown promising results in image generation tasks. Therefore, in this research work, we build on conditional GANs (CGAN) to generate plausible distributions of a given profile to solve the challenge of data imbalance in anomaly detection tasks and present a novel framework for anomaly detection. Firstly, we learn the pattern of the minority class data samples using a single class CGAN. Secondly, we use the knowledge base of the single class CGAN to generate samples that augment the minority class samples so that a binary class CGAN can train on the typical and malicious profiles with a balanced dataset. This approach inherently eliminates the bias imposed on algorithms from the dataset and results in a robust framework with improved generalization. Thirdly, the binary class CGAN generates a knowledge base that we use to construct the cluster based anomaly detector. During testing, we do not use the single class CGAN, thereby providing us with a lean and efficient algorithm for anomaly detection that can do anomaly detection on semi supervised and non parametric multivariate data. We test the framework on logs and image based anomaly detection datasets with class imbalance. We compare the performance of AD CGAN with GAN derived and non GAN derived state of the art algorithms on benchmark datasets. AD CGAN outperforms most of the algorithms in the standard metrics of Precision, Recall, and F 1 Score. Where AD CGAN does not perform better in the parameters used, it has the advantage of being lightweight. Therefore, it can be deployed for both online and offline anomaly detection tasks since it does not use an input sample inversion strategy.
IEEE Access