Classification Rule Discovery Using Ant-Miner Algorithm: An Application Of Network Intrusion Detection
Journal Title: International Journal of Modern Engineering Research (IJMER) - Year 2014, Vol 4, Issue 8
Abstract
Enormous studies on intrusion detection have widely applied data mining techniques to finding out the useful knowledge automatically from large amount of databases, while few studies have proposed classification data mining approaches. In an actual risk assessment process, the discovery of intrusion detection prediction knowledge from experts is still regarded as an important task because experts’ predictions depend on their subjectivity. Traditional statistical techniques and artificial intelligence techniques are commonly used to solve this classification decision making. This paper proposes an ant-miner based data mining method for discovering network intrusion detection rules from large dataset. The obtained result of this experiment shows that clearly the ant-miner is superior than ID3, J48, ADtree, BFtree, Simple cart. Although different classification models have been developed for network intrusion detection, each of them has its strength and weakness, including the most commonly applied Support Vector Machine(SVM)method and the clustering based on Self Organized Ant Colony Network (CSOACN).Our algorithm is implemented and evaluated using a standard bench mark KDD99 dataset. Experiments show that ant-miner algorithm out performs than other methods in terms of both classification rate and accuracy.
Authors and Affiliations
J. Uthayakumar , D. Nivetha , D. Vinotha , M. Vasanthi
Keywords
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