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A Comprehensive Analysis and study in Intrusion Detection System using Data Mining Techniques

Author(s): G. V. Nadiammai | S. Krishnaveni | M. Hemalatha

Journal: International Journal of Computer Applications
ISSN 0975-8887

Volume: 35;
Issue: 8;
Start page: 51;
Date: 2011;
Original page

Keywords: Data Mining | Intrusion Detection | Machine Learning | Zero R Decision Table | Random Forest classifier | KDDCup99 dataset

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 and 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 and Random Forest classifier with KDDCUP 99 databases from MIT Lincoln Laboratory.
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