A fast all-packets-based DDoS attack detection approach based on network graph and graph kernel. (1st July 2021) - Liu, Xinqian; Ren, Jiadong; He, Haitao; Zhang, Bing; Song, Chen; Wang, Yunxue
Abstract: DDoS attack detection methods play a very important role in protecting computer network security. However, the existing flow-based DDoS attack detection methods face the non-negligible time delay and are not general for different types of DDoS attacks at different rates. In order to fill this research gap, a f ast a ll-p ackets-based D DoS attack d etection approach (FAPDD) is proposed. The FAPDD firstly designs a new time series network graph model to effectively simplify the processing of network traffic handling compared with the flow-based detections. Furthermore, it is the first time that the directed Weisfeiler-Lehman graph kernel is built for measuring the divergence between the current network graph and the normalization network graphs. Due to the new graph model and kernel measurement method to judge network changes, the different types and rates of DDoS attacks can be especially detected. In addition, the dynamic threshold and freezing mechanism are constructed to display standard traffic changes and prevent the pollution of attack traffic to the standard network. Finally, a number of real DDoS attack datasets are applied to evaluate the effectiveness of the proposed method, as well as the overall time efficiency and detection effect. Compared with other methods, the FAPDD can better meet the real-time requirements and achieve good detection effects in different types of DDoS attacks with different attack rates. Graphical abstract: Figure 2 The overall frame of the fast DDoS attack detection approach. Image 1 Highlights: It is the first time to propose a fast DDoS attack detection method to achieve the real-time online attack detection. Based on the interaction between network packets, a simplified network graph model is proposed. A novel directed WL graph kernel method is proposed to measure the divergence between the current network graph and NNGs. The dynamic threshold and freezing mechanism are designed to detect and judge the network divergence. The FAPDD can detect different types of DDoS attacks with different attack rates by evaluating two datasets.