Title: Experimental analysis of application-level intrusion detection algorithms

Authors: Yuhong Dong, Sam Hsu, Saeed Rajput, Bing Wu

Addresses: Department of Advanced Technologies, Alcorn State University, Alcorn State, MS 39096, USA. ' Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL 33431, USA. ' Division of Math, Science and Technology, Nova Southeastern University, Florida 33314, USA. ' Department of Mathematics and Computer Science, Fayetteville State University, Fayetteville, NC 28301, USA

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.

Keywords: IDS; intrusion detection system; anomaly detection; network security; information security; graph-based sequence learning.

DOI: 10.1504/IJSN.2010.032218

International Journal of Security and Networks, 2010 Vol.5 No.2/3, pp.198 - 205

Available online: 17 Mar 2010 *

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