Security Situation Assessment Model of DDoS Attack Based on Progressive Fuzzy C Clustering Algorithm
Yao Hu, Bibo Tu · 2024
As Software Defined Network (SDN) is widely used, the risk of Distributed Denial of Service (DDoS) attacks on SDN is increasing. The attack traffic generated by DDoS attacks puts huge load pressure on the SDN network, affecting normal network services. In severe cases, the entire SDN network may break down and property damage may be caused. Therefore, the detection of attack mode is very necessary and of great significance. Therefore, this paper proposes an attack detection method based on Fuzzy C-means clustering algorithm (FCM). A DDoS attack detection and defense system is designed, which includes data collection, attack detection and attack defense. Finally, experiments are conducted to verify the effectiveness of the proposed DDoS attack detection algorithm and defense strategy. The experimental results show that the error rate and error rate of FCM fuzzy clustering algorithm are only 4.15% and 3.75%, which has obvious advantages compared with other commonly used detection methods.