| Title | A CGAN-based DDoS Attack Detection Method in SDN |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Liu, Luo, Jiang, Wang, Li, Jia |
| Conference Name | 2021 International Wireless Communications and Mobile Computing (IWCMC) |
| Keywords | attack detection, Conditional generative adversarial network (CGAN), DDoS attack detection, denial-of-service attack, Distributed Denial of Service (DDoS), feature extraction, generative adversarial networks, Human Behavior, machine learning algorithms, Metrics, Neural networks, pubcrawl, resilience, Resiliency, Soft defined network (SDN), Training, Wireless communication |
| Abstract | Distributed denial of service (DDoS) attack is a common way of network attack. It has the characteristics of wide distribution, low cost and difficult defense. The traditional algorithms of machine learning (ML) have such shortcomings as excessive systemic overhead and low accuracy in detection of DDoS. In this paper, a CGAN (conditional generative adversarial networks, conditional GAN) -based method is proposed to detect the attack of DDoS. On off-line training, five features are extracted in order to adapt the input of neural network. On the online recognition, CGAN model is adopted to recognize the packets of DDoS attack. The experimental results demonstrate that our proposed method obtains the better performance than the random forest-based method. |
| DOI | 10.1109/IWCMC51323.2021.9498840 |
| Citation Key | liu_cgan-based_2021 |
A CGAN-based DDoS Attack Detection Method in SDN