Polymorphic Adversarial Cyberattacks Using WGAN

Journal of Cybersecurity and Privacy

Alireza Izaddoost¹  Shahram Shah Heydari²  Ravi Chauhan²  Ulya Sabeel² 

[1] Department of Computer Science, California State University-Dominguez Hills, Carson, CA 90747, USA;Faculty of Business and IT, University of Ontario Institute of Technology, Oshawa, ON L1G 0C5, Canada;

关键词: adversarial attacks;    Generative Adversarial Network (GAN);    Intrusion Detection Systems;    DDoS/DoS attacks;    machine learning;    Wasserstein Generative Adversarial Network (WGAN);   

DOI  :  10.3390/jcp1040037

来源: DOAJ

【 摘 要 】

Intrusion Detection Systems (IDS) are essential components in preventing malicious traffic from penetrating networks and systems. Recently, these systems have been enhancing their detection ability using machine learning algorithms. This development also forces attackers to look for new methods for evading these advanced Intrusion Detection Systemss. Polymorphic attacks are among potential candidates that can bypass the pattern matching detection systems. To alleviate the danger of polymorphic attacks, the IDS must be trained with datasets that include these attacks. Generative Adversarial Network (GAN) is a method proven in generating adversarial data in the domain of multimedia processing, text, and voice, and can produce a high volume of test data that is indistinguishable from the original training data. In this paper, we propose a model to generate adversarial attacks using Wasserstein GAN (WGAN). The attack data synthesized using the proposed model can be used to train an IDS. To evaluate the trained IDS, we study several techniques for updating the attack feature profile for the generation of polymorphic data. Our results show that by continuously changing the attack profiles, defensive systems that use incremental learning will still be vulnerable to new attacks; meanwhile, their detection rates improve incrementally until the polymorphic attack exhausts its profile variables.

【 授权许可】

Unknown