DDoS Attacks Detection with AutoEncoder
Kun Yang, Junjie Zhang, Yang Xu, Jonathan Chao
Abstract
Although many distributed denial of service (DDoS) attacks detection algorithms have been proposed and even some of them have claimed high detection accuracy, DDoS attacks are still a major problem for network security. The latent and inherent problems of these detection algorithms are 1) Requirement of both normal and attack data for building detection models, and 2) Almost inability to detect novel and unknown DDoS attacks. To conquer the problems, this paper proposes an AutoEncoder based DDoS attacks Detection Framework (AE-D3F), which only uses normal traffic to build the detection model and is able to update itself automatically as time goes. Experimental results on synthetic and public traffic show that our AE-D3F can not only achieve 82.00% detection rate (DR) with 0 false positive rate (FPR), better than classical anomaly detection approaches, but also detect novel and unknown attacks.