Computer Science ›› 2018, Vol. 45 ›› Issue (12): 123-129.doi: 10.11896/j.issn.1002-137X.2018.12.019

• Information Security • Previous Articles     Next Articles

DDoS Attack Detection System Based on Intelligent Bee Colony Algorithm

YU Xue-shan1, HAN De-zhi1, DU Zheng-xin1,2   

  1. (College of Information Engineering,Shanghai Maritime University,Shanghai 201306,China)1
    (School of Computer Information Engineering,Hanshan Normal University,Chaozhou,Guangdong 521041,China)2
  • Received:2017-11-15 Online:2018-12-15 Published:2019-02-25

Abstract: With the popularity of the applications of big data,DDoS attacks become increasingly serious and have been the main network security issues.This paper designed a DDoS attack intrusion detection system based on clustering and intelligent bee colony algorithm (DFSABC_elite) for DDoS attack detection in environment of big data.The system combines the clustering algorithm and the intelligent bee colony algorithm to classify DDoS attack data flow,and uses the traffic feature distribution entropy and the generalized likelihood comparison distinguishing factor together to detect the characteristics of DDoS attack data stream,thus achieving the efficient detection of DDoS attack data flow.Experimental results show that this system is obviously superior to the ordinary bee colony algorithm based on parallelization K-means and the DDOS detection algorithm based on parallelization K-means in terms of intra-class compactness,inter-class separation,clustering accuracy,consumed time and DDoS detection accuracy.

Key words: Clustering algorithm, DDoS attack, Generalized likelihood comparison, Intelligent bee colony algorithm, Traffic feature distribution entropy

CLC Number: 

  • TP309.2
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