Items by Wang, Wei

Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we pro- pose a novel general framework for adaptive and online de- tection 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 “con- cept 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 detec- tion 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 col- lected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.

The abundant knowledge in web communities has motivated the research interests in discussion threads. The dynamic nature of discussion threads poses interesting and challenging problems for computer scientists. Although techniques such as semantic models or structural models have been shown to be useful in a number of areas, they are inefficient in understanding discussion threads due to the temporal dependence among posts in a discussion thread. Such dependence causes that semantics and structure coupled with each other in discussion threads. In this paper, we propose a sparse coding-based model named SMSS to Simultaneously Model Semantic and Structure of discussion threads.