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Adaptive Intrusion Detection System via online machine learning

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2 Author(s)
Hai Thanh Nguyen ; Dept. of Comput. Sci. & Media Technol., Gjovik Univ. Coll., Gjovik, Norway ; Franke, K.

Adaptation of Intrusion Detection Systems (IDSs) in the heterogeneous and adversarial network environments is crucial. We design an adaptive IDS that has 10% higher accuracy than the best of four different baseline IDSs. Rather than creating a new `super' IDS, we combine the outputs of the IDSs by using the online learning framework proposed by Bousquet and Warmuth [1]. The combination framework allows to dynamically determine the best IDSs performed in different segments of a dataset. Moreover, to increase the accuracy and reliability of the intrusion detection results, the fusion between outputs of the four IDSs is taken into account by a new expanded framework. We conduct the experiments on two different datasets for benchmarking Web Application Firewalls: the ECML-PKDD 2007 HTTP dataset and the CISIC HTTP 2010. Experimental results show the high adaptability of the proposed IDS.

Published in:

Hybrid Intelligent Systems (HIS), 2012 12th International Conference on

Date of Conference:

4-7 Dec. 2012