A comparative analysis of CGAN‐based oversampling for anomaly detection
Rahbar Ahsan, Wei Shi, Xiangyu Ma, William Lee Croft
Abstract
Abstract In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data‐level and algorithm‐level approaches into account to cope with the class‐imbalance problem is proposed. This solution integrates the auto‐learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN‐based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN‐based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.