International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 35 - Issue 8 |
Published: December 2011 |
Authors: G. V. Nadiammai, S. Krishnaveni, M. Hemalatha |
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G. V. Nadiammai, S. Krishnaveni, M. Hemalatha . A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques. International Journal of Computer Applications. 35, 8 (December 2011), 51-56. DOI=10.5120/4425-6161
@article{ 10.5120/4425-6161, author = { G. V. Nadiammai,S. Krishnaveni,M. Hemalatha }, title = { 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 }, doi = { 10.5120/4425-6161 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2011 %A G. V. Nadiammai %A S. Krishnaveni %A M. Hemalatha %T A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques%T %J International Journal of Computer Applications %V 35 %N 8 %P 51-56 %R 10.5120/4425-6161 %I Foundation of Computer Science (FCS), NY, USA
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.