A general framework for adaptive and online detection of web attacks

Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with “concept drift”in web audit data streams. Str-DBSCAN that we extended DBSCAN [1] to streaming data as well as StrAP [3] are both used to validate the framework. The detection model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness. Categories and Subject Descriptors C.2.0 [Computer-Communication Networks]: General— Security and protectio...

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Wei Wang 0012, Florent Masseglia, Thomas Guyet, Rene Quiniou, Marie-Odile Cordier
• Keywords: Detection, General Framework, Internet Technology, Web Audit Data, WWW 2009