We propose a novel framework of autonomic intrusion detection that fulfills online and adaptive intrusion detection in unlabeled audit data streams. The framework owns ability of self-managing: self-labeling, self-updating and self-adapting. Affinity Propagation (AP) uses the framework to learn a subject's behavior through dynamical clustering of the streaming data. The testing results with a large real HTTP log stream demonstrate the effectiveness and efficiency of the method. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Wang, W., Guyet, T., & Knapskog, S. J. (2009). Autonomic intrusion detection system. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5758 LNCS, pp. 359–361). https://doi.org/10.1007/978-3-642-04342-0_24
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