A Classification Detection Algorithm Based on Joint Entropy Vector against Application-Layer DDoS Attack

Abstract EN

The application-layer distributed denial of service (AL-DDoS) attack makes a great threat against cyberspace security.

The attack detection is an important part of the security protection, which provides effective support for defense system through the rapid and accurate identification of attacks.

According to the attacker’s different URL of the Web service, the AL-DDoS attack is divided into three categories, including a random URL attack and a fixed and a traverse one.

In order to realize identification of attacks, a mapping matrix of the joint entropy vector is constructed.

By defining and computing the value of EUPI and jEIPU, a visual coordinate discrimination diagram of entropy vector is proposed, which also realizes data dimension reduction from N to two.

In terms of boundary discrimination and the region where the entropy vectors fall in, the class of AL-DDoS attack can be distinguished.

Through the study of training data set and classification, the results show that the novel algorithm can effectively distinguish the web server DDoS attack from normal burst traffic.