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A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques
International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 35 - Number 8
Year of Publication: 2011
|
10.5120/4425-6161 |
G V Nadiammai, S Krishnaveni and M Hemalatha. Article: A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques. International Journal of Computer Applications 35(8):51-56, December 2011. Full text available. BibTeX
@article{key:article,
author = {G. V. Nadiammai and S. Krishnaveni and M. Hemalatha},
title = {Article: A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques},
journal = {International Journal of Computer Applications},
year = {2011},
volume = {35},
number = {8},
pages = {51-56},
month = {December},
note = {Full text available}
}
Abstract
Data mining refers to extracting knowledge from large amounts of data. Most of the current systems are weak at detecting attacks without generating false alarms. Intrusion detection systems (IDSs) are increasingly a key part of system defense. An intrusion can be defined as any set of actions that compromise the integrity, confidentiality or availability of a network resource(such as user accounts, file system, kernels & so on).Data mining plays a prominent role in data analysis. In this paper, classification techniques are used to predict the severity of attacks over the network. I have compared zero R classifier, Decision table classifier & Random Forest classifier with KDDCUP 99 databases from MIT Lincoln Laboratory.
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