Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Today most information interchanges are performed in the internet. In the environment, internet attacks continue to increase. So, the methods used as intrusion detective tools for protecting network systems against diverse attacks are very important. The skills of intrusion are getting more powerful continuously. However, the detection techniques have been hard to catch up with these attacks. Therefore, we need good tools for intrusion detection. Many researches for intrusion detection have been studied. Most of them had a difficulty in classifying intrusions from networks accesses in the case of new patterns of intrusions which were not experienced by predictive models. In this study, we propose an efficient method to settle the problem. Our model is constructed by combining evolutionary programming into support vector machine. This model is able to detect new attacks as well as experienced attacks. We verify an improved performance of our model using KDD Cup-99 task data designed by DARPA.
How to cite this article:
Sung-Hae Jun and Kyung-Whan Oh . An Evolutionary Support Vector Machine for Intrusion Detection.
DOI: https://doi.org/10.36478/ajit.2006.778.783
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2006.778.783