| Title | Adversarial Attack and Defense on Graph-based IoT Botnet Detection Approach |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Ngo, Quoc-Dung, Nguyen, Huy-Trung, Nguyen, Viet-Dung, Dinh, Cong-Minh, Phung, Anh-Tu, Bui, Quy-Tung |
| Conference Name | 2021 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) |
| Date Published | jun |
| Keywords | adversarial attack, Attack Graphs, Botnet, Classification algorithms, composability, Computational modeling, graph analysis, Human Behavior, IoT Botnet detection, Malware, Malware Analysis and Graph Theory, Predictive Metrics, privacy, pubcrawl, reinforcement learning, Resiliency, supervised learning, Training |
| Abstract | To reduce the risk of botnet malware, methods of detecting botnet malware using machine learning have received enormous attention in recent years. Most of the traditional methods are based on supervised learning that relies on static features with defined labels. However, recent studies show that supervised machine learning-based IoT malware botnet models are more vulnerable to intentional attacks, known as an adversarial attack. In this paper, we study the adversarial attack on PSI-graph based researches. To perform the efficient attack, we proposed a reinforcement learning based method with a trained target classifier to modify the structures of PSI-graphs. We show that PSI-graphs are vulnerable to such attack. We also discuss about defense method which uses adversarial training to train a defensive model. Experiment result achieves 94.1% accuracy on the adversarial dataset; thus, shows that our defensive model is much more robust than the previous target classifier. |
| DOI | 10.1109/ICECCE52056.2021.9514255 |
| Citation Key | ngo_adversarial_2021 |
Adversarial Attack and Defense on Graph-based IoT Botnet Detection Approach