Fusions of GA and SVM for anomaly detection in intrusion detection system

Authors:
Dong Seong Kim;Ha-Nam Nguyen;Syng-Yup Ohn;Jong Sou Park
Affiliations:
Computer Engineering Department, Hankuk Aviation University;Computer Engineering Department, Hankuk Aviation University;Computer Engineering Department, Hankuk Aviation University;Computer Engineering Department, Hankuk Aviation University
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 7
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Abstract

It is important problems to increase the detection rates and reduce false positive rates in Intrusion Detection System (IDS). These problems can be viewed as optimization problems for features and parameters for a detection model in IDS. This paper proposes fusions of Genetic Algorithm (GA) and Support Vector Machines (SVM) for efficient optimization of both features and parameters for detection models. Our method provides optimal anomaly detection model which is capable to minimize amounts of features and maximize the detection rates. In experiments, we show that the proposed method is efficient way of selecting important features as well as optimizing the parameters for detection model and provides more stable detection rates.