  Our increasingly connected world continues to face an ever-growing amount of
network-based attacks. Intrusion detection systems (IDS) are an essential
security technology for detecting these attacks. Although numerous machine
learning-based IDS have been proposed for the detection of malicious network
traffic, the majority have difficulty properly detecting and classifying the
more uncommon attack types. In this paper, we implement a novel hybrid
technique 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 as
well as normal network flow. Ultimately, our findings demonstrate that training
the DRL on specific synthetic datasets can result in better performance in
correctly classifying minority classes over training on the true imbalanced
dataset.

Cryptography and Security;Artificial Intelligence;Networking and Internet Architecture