Association rule mining in intrusion detection systems

Dong Zhao; Yan-sheng Lu

doi:10.1117/12.518822

15 April 2004 Association rule mining in intrusion detection systems

Dong Zhao, Yan-sheng Lu

Author Affiliations +

Proceedings Volume 5282, Network Architectures, Management, and Applications; (2004) https://doi.org/10.1117/12.518822
Event: Asia-Pacific Optical and Wireless Communications, 2003, Wuhan, China

Abstract

In a modern computer system, intrusion detection has become an essential and critical component. Data mining generally refers to the process of extracting models from large stores of data. The intrusion detection system first apply data mining programs to audit data to compute frequent patterns, extract features, and then use classification algorithms to compute detection models. The most important step of this process is to determine relations between fields in the database records to construct features. The standard association rules have not enough expressiveness. Intrusion detection system can extract the association rule with negations and with varying support thresholds to get better performance rather than extract the standard association rule.

Citation Download Citation

Dong Zhao and Yan-sheng Lu "Association rule mining in intrusion detection systems", Proc. SPIE 5282, Network Architectures, Management, and Applications, (15 April 2004); https://doi.org/10.1117/12.518822

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