JOURNAL OF SOFTWARE, VOL. 9, NO. 4, APRIL 2014 985 Mining Web User Behaviors to Detect Application Layer DDoS Attacks Chuibi Huang1 1Department of Automation, USTC Key laboratory of network communication system and control Hefei, China Email:
[email protected] Jinlin Wang1, 2, Gang Wu1, Jun Chen2 2Institute of Acoustics, Chinese Academy of Sciences National Network New Media Engineering Research Center Beijing, China Email: {wangjl, chenj}@dsp.ac.cn Abstract— Distributed Denial of Service (DDoS) attacks than TCP or UDP-based attacks, requiring fewer network have caused continuous critical threats to the Internet connections to achieve their malicious purposes. The services. DDoS attacks are generally conducted at the MyDoom worm and the CyberSlam are all instances of network layer. Many DDoS attack detection methods are this type attack [2, 3]. focused on the IP and TCP layers. However, they are not suitable for detecting the application layer DDoS attacks. In The challenges of detecting the app-DDoS attacks can this paper, we propose a scheme based on web user be summarized as the following aspects: browsing behaviors to detect the application layer DDoS • The app-DDoS attacks make use of higher layer attacks (app-DDoS). A clustering method is applied to protocols such as HTTP to pass through most of extract the access features of the web objects. Based on the the current anomaly detection systems designed access features, an extended hidden semi-Markov model is for low layers. proposed to describe the browsing behaviors of web user. • Along with flooding, App-DDoS traces the The deviation from the entropy of the training data set average request rate of the legitimate user and uses fitting to the hidden semi-Markov model can be considered as the abnormality of the observed data set.