Robust Data Clustering Algorithms for Network Intrusion Detection
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International Journal of Computer & Organization Trends (IJCOT) |  |
| © 2012 by IJCOT Journal | ||
| Volume-2 Issue-5 |
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| Year of Publication : 2012 | ||
| Authors : Gunja Ambica , Mrs.N.Rajeswari |
Citation
Gunja Ambica , Mrs.N.Rajeswari "Robust Data Clustering Algorithms for Network Intrusion Detection" . International Journal of Computer & organization Trends (IJCOT), V2(5):6-11 Sep - Oct 2012, ISSN 2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract
IDS (Intrusion Detection system) is an active and driving defense technology. Intrusion detection is to detect attacks against a computer system. This project mainly focuses on intrusion detection based on data mining. Data mining is to identify valid, novel, potentially useful, and ultimately understandable patterns in massive data. One of the primary challenges to intrusion detection are the problem of misjudgment, misdetection and lack of real time response to the attack. In the recent years, as the second line of defense after firewall This project presents an approach to detect intrusion based on data mining frame work. In this framework, intrusion detection is achieved using clustering techniques. Firstly, a method to reduce the noise in the data set using improved kmeans. This system use K-means,FCM and Improved K-means data mining algorithms are used to improves the performance of intrusion detection since the traffic is large and the types of attack are various. By the more accurate method of finding k clustering center, an anomaly detection model was presented to get better detection effect. This project used KDD CUP 1999 data set to test the performance of the model. The results show the system has a higher detection rate and a lower false alarm rate, it achieves expectant aim.
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