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.
CITATION STYLE
Ahsan, R., Shi, W., Ma, X., & Lee Croft, W. (2022). A comparative analysis of CGAN-based oversampling for anomaly detection. IET Cyber-Physical Systems: Theory and Applications, 7(1), 40–50. https://doi.org/10.1049/cps2.12019
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