| Title | Polymorphic Adversarial DDoS attack on IDS using GAN |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Chauhan, R., Heydari, S. Shah |
| Conference Name | 2020 International Symposium on Networks, Computers and Communications (ISNCC) |
| Keywords | adversarial attacks, adversarial data, adversarial DDoS attacks, attack profile, computer network security, Data models, DDoS Attacks, defensive systems, feature extraction, gan, Generative Adversarial Learning, generative adversarial networks, generative adversarial networks (GAN), Generators, IDS, incremental learning, Intrusion detection, Intrusion Detection Systems, learning (artificial intelligence), machine learning, machine learning algorithms, Malicious Traffic, Malware, natural language processing, polymorphic Adversarial DDoS attack, Predictive Metrics, pubcrawl, Resiliency, Scalability, security of data, telecommunication security, Training, unknown types |
| Abstract | Intrusion Detection systems are important tools in preventing malicious traffic from penetrating into networks and systems. Recently, Intrusion Detection Systems are rapidly enhancing their detection capabilities using machine learning algorithms. However, these algorithms are vulnerable to new unknown types of attacks that can evade machine learning IDS. In particular, they may be vulnerable to attacks based on Generative Adversarial Networks (GAN). GANs have been widely used in domains such as image processing, natural language processing to generate adversarial data of different types such as graphics, videos, texts, etc. We propose a model using GAN to generate adversarial DDoS attacks that can change the attack profile and can be undetected. Our simulation results indicate that by continuous changing of attack profile, defensive systems that use incremental learning will still be vulnerable to new attacks. |
| DOI | 10.1109/ISNCC49221.2020.9297264 |
| Citation Key | chauhan_polymorphic_2020 |
Polymorphic Adversarial DDoS attack on IDS using GAN