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.
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Kim, D.S., Nguyen, HN., Ohn, SY., Park, J.S. (2005). Fusions of GA and SVM for Anomaly Detection in Intrusion Detection System. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_67
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DOI: https://doi.org/10.1007/11427469_67
Publisher Name: Springer, Berlin, Heidelberg
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