Network intrusion detection using adversarial computational intelligence

Conventional intrusion detection systems (IDSs) in network ecosystems frequently face difficulties in recognising new types of attacks and navigating intricate network architectures, often resulting in a high false positives. Over the past recent years, researchers have probed a range of machine learning and deep learning frameworks to tackle these challenges, although many of these models demand more labelled data than what is typically accessible. To mitigate the data scarcity issue, researchers have begun utilising generative adversarial networks (GANs), specifically the external classifier generative adversarial network (EC-GAN) approach, to generate synthetic data. Our study employs a deep neural network (DNN) classifier, trained using EC-GAN, and benchmarks its performance against both earlier research and traditional training techniques. Remarkably, the classifier that leveraged the EC-GAN method demonstrated superior performance compared to other investigations, even though it required only a small portion of the original training dataset. To judge the computational novelty, comparative analysis and bench-marking is also carried out.