A new framework for intrusion detection based on rough set theory

Zhijun Li; Yu Wu; Guoyin Wang; Yongjun Hai; Yunpeng He

doi:10.1117/12.540951

12 April 2004 A new framework for intrusion detection based on rough set theory

Zhijun Li, Yu Wu, Guoyin Wang, Yongjun Hai, Yunpeng He

Proceedings Volume 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI; (2004) https://doi.org/10.1117/12.540951
Event: Defense and Security, 2004, Orlando, Florida, United States

Abstract

Intrusion detection is an essential component of critical infrastructure protection mechanism. Since many current IDSs are constructed by manual encoding of expert knowledge, it is time-consuming to update their knowledge. In order to solve this problem, an effective method for misuse intrusion detection with low cost and high efficiency is presented. This paper gives an overview of our research in building a detection model for identifying known intrusions, their variations and novel attacks with unknown natures. The method is based on rough set theory and capable of extracting a set of detection rules from network packet features. After getting a decision table through preprocessing raw packet data, rough-set-based reduction and rule generation algorithms are applied, and useful rules for intrusion detection are obtained. In addition, a rough set and rule-tree-based incremental knowledge acquisition algorithm is presented in order to solve problems of updating rule set when new attacks appear. Compared with other methods, our method requires a smaller size of training data set and less effort to collect training data. Experimental results demonstrate that our system is effective and more suitable for online intrusion detection.

Citation Download Citation

Zhijun Li, Yu Wu, Guoyin Wang, Yongjun Hai, and Yunpeng He "A new framework for intrusion detection based on rough set theory", Proc. SPIE 5433, Data Mining and Knowledge Discovery: Theory, Tools, and Technology VI, (12 April 2004); https://doi.org/10.1117/12.540951

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