Research on Ddos Attack Security Situation Assessment Model Based on Fuzzy C Clustering Algorithm

EasyChair Preprint 15434

Abstract

As SDN (Software Defined Network) becomes more and more widely used, the risk of SDN network facing DDoS (Distributed Denial of Service, Distributed Denial of Service) attacks is also increasing. The attack traffic generated by DDoS attacks will cause serious damage to SDN. The network brings huge load pressure, which affects the normal network business. In severe cases, it may cause the entire SDN network to be paralyzed and cause property losses. Therefore, the detection of attack methods is very necessary and of great significance. This paper proposes an attack detection method based on Fuzzy C-means clustering algorithm (FCM), and designs a DDoS attack detection and defense system that includes three major functions: data collection, attack detection, and attack defense. Finally, the effectiveness of the proposed DDoS attack detection algorithm and defense strategy was verified through experiments. Experimental results show that the missed detection rate and bit error rate of the FCM fuzzy clustering algorithm are only 4.15% and 3.75%, which have obvious advantages over other commonly used detection methods.

Keyphrases: DDoS attack, Network Security, fuzzy c-means clustering algorithm, security situation prediction

@booklet{EasyChair:15434,
  author    = {Yao Hu and Xiaolin Chen and Yuexin Zhang},
  title     = {Research on Ddos Attack Security Situation Assessment Model Based on Fuzzy C Clustering Algorithm},
  howpublished = {EasyChair Preprint 15434},
  year      = {EasyChair, 2024}}