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Our increasingly connected world continues to face an ever-growing amount ofnetwork-based attacks. Intrusion detection systems (IDS) are an essentialsecurity technology for detecting these attacks. Although numerous machinelearning-based IDS have been proposed for the detection of malicious networktraffic, the majority have difficulty properly detecting and classifying themore uncommon attack types. In this paper, we implement a novel hybridtechnique using synthetic data produced by a Generative Adversarial Network(GAN) to use as input for training a Deep Reinforcement Learning (DRL) model.Our GAN model is trained with the NSL-KDD dataset for four attack categories aswell as normal network flow. Ultimately, our findings demonstrate that trainingthe DRL on specific synthetic datasets can result in better performance incorrectly classifying minority classes over training on the true imbalanceddataset.

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection