Heydari, ShahramChauhan, Ravi2021-02-262022-03-292021-02-262022-03-292020-12-01https://hdl.handle.net/10155/1249IDS are essential components in preventing malicious traffic from penetrating networks. IDS have been rapidly enhancing their detection ability using ML algorithms. As a result, attackers look for new methods to evade the IDS. Polymorphic attacks are favorites among the attackers as they can bypass the IDS. 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. I proposed a model to generate DDoS attacks using a WGAN. I used several techniques to update the attack feature profile and generate polymorphic data. This data will change the feature profile in every cycle to test if the IDS can detect the new version attack data. Simulation results from the proposed model show that by continuous changing of attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks.enAdversarial attacksGenerative Adversarial Networks (GAN)Intrusion detection systemDDoS attacksMachine learningThesis