Experimental analysis of application-level intrusion detection algorithms
by Yuhong Dong, Sam Hsu, Saeed Rajput, Bing Wu
International Journal of Security and Networks (IJSN), Vol. 5, No. 2/3, 2010

Abstract: Intrusion Detection System (IDS) plays a very important role on information security. In this paper, we present an application-level intrusion detection algorithm named Graph-based Sequence-Learning Algorithm (GSLA). GSLA includes data pre-processing, normal profile construction and session marking. In GSLA, the normal profile is built through a session-learning method, which is used to determine an anomaly session. We conduct experiments and evaluate the performance of GSLA with other conventional algorithms, such as Markov Chain Model (MM) and K-means Algorithm. The results show that GSLA improves the effectiveness of anomaly detection.

Online publication date: Wed, 17-Mar-2010

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