| Title | Detecting Adversarial DDoS Attacks in Software- Defined Networking Using Deep Learning Techniques and Adversarial Training |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Nugraha, Beny, Kulkarni, Naina, Gopikrishnan, Akash |
| Conference Name | 2021 IEEE International Conference on Cyber Security and Resilience (CSR) |
| Keywords | Adversarial Network Attacks, Adversarial training, anomaly detection, DDoS attack detection, Deep Learning, Degradation, denial-of-service attack, Flooding DDoS Attack, Human Behavior, Metrics, pubcrawl, Real-time Systems, resilience, Resiliency, Robustness, software defined networking, Software- Defined Networking, Training |
| Abstract | In recent years, Deep Learning (DL) has been utilized for cyber-attack detection mechanisms as it offers highly accurate detection and is able to overcome the limitations of standard machine learning techniques. When applied in a Software-Defined Network (SDN) environment, a DL-based detection mechanism shows satisfying detection performance. However, in the case of adversarial attacks, the detection performance deteriorates. Therefore, in this paper, first, we outline a highly accurate flooding DDoS attack detection framework based on DL for SDN environments. Second, we investigate the performance degradation of our detection framework when being tested with two adversary traffic datasets. Finally, we evaluate three adversarial training procedures for improving the detection performance of our framework concerning adversarial attacks. It is shown that the application of one of the adversarial training procedures can avoid detection performance degradation and thus might be used in a real-time detection system based on continual learning. |
| DOI | 10.1109/CSR51186.2021.9527967 |
| Citation Key | nugraha_detecting_2021 |
Detecting Adversarial DDoS Attacks in Software- Defined Networking Using Deep Learning Techniques and Adversarial Training