Abstract
Intrusion detection system (IDS) is one of the important research area in field of information and network security to protect information or data from unauthorized access. IDS is a classifier that can classify the data as normal or attack. In this paper, we have focused on many existing feature selection techniques to remove irrelevant features from NSL-KDD data set to develop a robust classifier that will be computationally efficient and effective. Four different feature selection techniques :Info Gain, Correlation, Relief and Symmetrical Uncertainty are combined with C4.5 decision tree technique to develop IDS . Experimental works are carried out using WEKA open source data mining tooland obtained results show that C4.5 with Info Gain feature selection technique has produced highest accuracy of 99.68% with 17 features, however result obtain in case of Symmetrical Uncertainty with C4.5 is also promising with 99.64% accuracy in case of only 11 features . Results are better as compare to the work already done in this area.
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Hota, H.S., Shrivas, A.K.: Data Mining Approach for Developing Various Models Based on Types of Attack and Feature Selection as Intrusion Detection Systems (IDS). In: Intelligent Computing, Networking, and Informatics. AISC, vol. 243, pp. 845–851. Springer, Heidelberg (2014)
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Hota, H.S., Shrivas, A.K. (2014). Decision Tree Techniques Applied on NSL-KDD Data and Its Comparison with Various Feature Selection Techniques. In: Kumar Kundu, M., Mohapatra, D., Konar, A., Chakraborty, A. (eds) Advanced Computing, Networking and Informatics- Volume 1. Smart Innovation, Systems and Technologies, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-07353-8_24
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DOI: https://doi.org/10.1007/978-3-319-07353-8_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07352-1
Online ISBN: 978-3-319-07353-8
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