Abstract
Intrusion Detection System (IDS) is the science of detection of malicious activity on a computer network. Due to the enormous volume existing and newly appearing network data, Data Mining classification methods are used for Intrusion Detection System. In this paper the classifying methods used are ID3, J48, Naive Bayes and OneR. The data set used for this experiment is kddcup1999. The dimensionality reduction is being performed from 41 attributes to 7 and 14 attributes based on Best First Search method and the 4 classifying methods are being applied. The result shows that ID3 and J48 method carry the highest accuracy and sensitivity with 7 and 14 attributes. Naive Bayes holds the highest degree of specification for all three dimensionalities. OneR has the worst Sensitivity with 7 and 14 attributes but the time taken by OneR for classification is very less. It is found that the optimal algorithm may vary based on the dimensionality.
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Kavitha, B., Karthikeyan, S., Chitra, B. (2010). Efficient Intrusion Detection with Reduced Dimension Using Data Mining Classification Methods and Their Performance Comparison. In: Das, V.V., et al. Information Processing and Management. BAIP 2010. Communications in Computer and Information Science, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_17
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DOI: https://doi.org/10.1007/978-3-642-12214-9_17
Publisher Name: Springer, Berlin, Heidelberg
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