A New Mimicking Attack by LSGAN
2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI)(2017)
摘要
Discriminating Distributed Denial of Service Attacks (DDoS) from Flash Crowds (FC) is a tough and challenging problem. If attackers could generate mimicking traffic which have little difference from the traffic produced by legitimate users in FC, are existing methods and defense systems still able to distinguish DDoS from FC? To verify the possibility of the existence of this mimicking attack and prove the existing methods cannot discriminate this attack from FC, this paper proposes an idea employed Least Squares Generative Adversarial Networks (LSGAN) to generate mimicking traffic based on a statistical features achieved from an extensive analysis of user traffic behavior of DDoS and FC. Then to establish an efficient defense system employed Random Forest to prove it can achieve better performance on real network traffic traces, but cannot discriminate this mimicking attack traffic from FC. The experiments results show the proposed idea can generate this mimicking attack traffic and the defense system cannot discriminate it from FC. In addition, a comparison with GAN has been made to show that LSGAN is better than GAN in performance.
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关键词
Flash Crowds,Mimicking Attacks,DDoS,LSGAN,GAN
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