Issue No. 05 - May (2012 vol. 24)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2011.17
Jose Antonio Iglesias , Carlos III University of Madrid, Madrid
Plamen Angelov , Lancaster University, Lancaster
Agapito Ledezma , Carlos III University of Madrid, Madrid
Araceli Sanchis , Carlos III University of Madrid, Madrid
Knowledge about computer users is very beneficial for assisting them, predicting their future actions or detecting masqueraders. In this paper, a new approach for creating and recognizing automatically the behavior profile of a computer user is presented. In this case, a computer user behavior is represented as the sequence of the commands she/he types during her/his work. This sequence is transformed into a distribution of relevant subsequences of commands in order to find out a profile that defines its behavior. Also, because a user profile is not necessarily fixed but rather it evolves/changes, we propose an evolving method to keep up to date the created profiles using an Evolving Systems approach. In this paper, we combine the evolving classifier with a trie-based user profiling to obtain a powerful self-learning online scheme. We also develop further the recursive formula of the potential of a data point to become a cluster center using cosine distance, which is provided in the Appendix. The novel approach proposed in this paper can be applicable to any problem of dynamic/evolving user behavior modeling where it can be represented as a sequence of actions or events. It has been evaluated on several real data streams.
Evolving fuzzy systems, fuzzy-rule-based (FRB) classifiers, user modeling.
A. Ledezma, J. A. Iglesias, P. Angelov and A. Sanchis, "Creating Evolving User Behavior Profiles Automatically," in IEEE Transactions on Knowledge & Data Engineering, vol. 24, no. , pp. 854-867, 2011.