Data mining-based intrusion detectors

Authors:
Su-Yun Wu;Ester Yen
Affiliations:
Department of Information Management, Vanaung University, Taiwan;Mathematical Sciences Research Institute, Berkeley, CA 94720-5070, USA
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 7
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Abstract

With popularization of internet, internet attack cases are increasing, and attack methods differs each day, thus information safety problem has became a significant issue all over the world. Nowadays, it is an urgent need to detect, identify and hold up such attacks effectively. The research intends to compare efficiency of machine learning methods in intrusion detection system, including classification tree and support vector machine, with the hope of providing reference for establishing intrusion detection system in future. Compared with other related works in data mining-based intrusion detectors, we proposed to calculate the mean value via sampling different ratios of normal data for each measurement, which lead us to reach a better accuracy rate for observation data in real world. We compared the accuracy, detection rate, false alarm rate for four attack types. More over, it shows better performance than KDD Winner, especially for U2R type and R2L type attacks.