Experimental analysis of application-level intrusion detection algorithms
Document Type
Article
Publication Date
1-1-2010
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. Copyright © 2010 Inderscience Enterprises Ltd.
Recommended Citation
Dong, Yuhong; Hsu, Sam; Rajput, Saeed; and Wu, Bing, "Experimental analysis of application-level intrusion detection algorithms" (2010). College of Health, Science, and Technology. 812.
https://digitalcommons.uncfsu.edu/college_health_science_technology/812