Design of DDoS attack detection system based on intelligent bee colony algorithm
Gongjun Yin, Qiuting Tian, Zhenxin Du, Xueshan Yu, Dezhi Han · International Journal of Computational Science and Engineering · 2019
As the large data applications gain popularity, distributed denial of service (DDoS) has become increasingly a serious major network security issue. In response to the problem of DDoS attack detection in big data environment, a DDoS attack detection system based on traffic reduction and intelligent artificial bee colony algorithm (EABC_elite) is designed. The system combines the traffic reduction algorithm and the intelligent bee colony algorithm to reduce the data traffic according to the idea of abnormal extraction. It uses the traffic feature distribution entropy and the generalised likelihood comparison discrimination factor to jointly detect the characteristics of DDoS attack data streams in order to quickly and efficiently achieve DDoS attack data flow accuracy detection. The experimental results show that the demand of traffic detection in this system is greatly reduced, the algorithm time-consuming and DDoS detection accuracy are obviously better than the separate traffic reduction algorithm and traffic reduction algorithm combined with common artificial bee colony algorithm.