Polymorphic Adversarial DDoS attack on IDS using GAN

IDS 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.

Name thesis:degree_name

Master of Science (MSc)

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Ontario Institute of Technology

Year dc:date.issued

2020

Author dc:creator

• Chauhan, Ravi

Advisor dc:contributor.advisor

• Heydari, Shahram

• Adversarial attacks
• Generative Adversarial Networks (GAN)
• Intrusion detection system
• DDoS attacks
• Machine learning

Language dc:language.iso

en

Handle dc:identifier.uri

https://hdl.handle.net/10155/1249

OAI identifier oai:identifier

oai:ontariotechu.scholaris.ca:10155/1249