Network Intrusion Detection System based on Generative Adversarial Network for Attack Detection

Abstract

The Intrusion Detection System (IDS) is the main element to prevent malicious traffic on the network. IDS will quickly increase the ability to detect network threats with the help of Deep Learning algorithms. As a result, attackers are finding new ways to evade identification. Polymorphic attacks, search for the attackers, as they can bypass the IDS. Generative Adversarial Networks (GAN) is a method proven in generating various forms of data. It is becoming popular among security researchers as it can produce indistinguishable data from the original data. This work proposed a model to generate DDoS attacks using a GAN. Several techniques have been used to regenerate the feature selection to identify the attack and generate polymorphic data. The data will change feature profile in every cycle to test if the IDS can detect the new version of attack data. Simulation results from the proposed model show that with constant changing attack profiles, defending arrangements that handle incremental knowledge will yet stay exposed to current attacks.