| Title | A Small Sample DDoS Attack Detection Method Based on Deep Transfer Learning |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | He, J., Tan, Y., Guo, W., Xian, M. |
| Conference Name | 2020 International Conference on Computer Communication and Network Security (CCNS) |
| Keywords | 8LANN network, Communication networks, composability, computer network security, DDoS attack detection, Deep Learning, deep learning detection technique, deep transfer learning, Degradation, denial-of-service attack, Human Behavior, learning (artificial intelligence), Measurement, Metrics, neural nets, Neural networks, Performance analysis, pubcrawl, resilience, Resiliency, small sample DDoS attack, small-sample DDoS attack detection, transfer learning |
| Abstract | When using deep learning for DDoS attack detection, there is a general degradation in detection performance due to small sample size. This paper proposes a small-sample DDoS attack detection method based on deep transfer learning. First, deep learning techniques are used to train several neural networks that can be used for transfer in DDoS attacks with sufficient samples. Then we design a transferability metric to compare the transfer performance of different networks. With this metric, the network with the best transfer performance can be selected among the four networks. Then for a small sample of DDoS attacks, this paper demonstrates that the deep learning detection technique brings deterioration in performance, with the detection performance dropping from 99.28% to 67%. Finally, we end up with a 20.8% improvement in detection performance by deep transfer of the 8LANN network in the target domain. The experiment shows that the detection method based on deep transfer learning proposed in this paper can well improve the performance deterioration of deep learning techniques for small sample DDoS attack detection. |
| DOI | 10.1109/CCNS50731.2020.00019 |
| Citation Key | he_small_2020 |
A Small Sample DDoS Attack Detection Method Based on Deep Transfer Learning