Abstract
Network Intrusion detection systems have become unavoidable with the phenomenal rise in internet based security threats. Data mining technique based Intrusion Detection System, have the added advantage of processing large amount of data speedily. However, success rate is dependent on selecting the optimal set of features here. Given an optimal set of features and a good training data set, Bayesian classifier is known for its simplicity and high accuracy. On the other hand, clustering techniques have the flexibility to detect novel attacks even when training set is not present. Therefore, combining the results of both classification and clustering techniques can improve the performance of Intrusion Detection systems greatly. Our project aims at building flexible Intrusion Detection system by combining the advantages of Bayesian classifier and the genetic clustering algorithm. It was tested with KDD Cup 1999 dataset by supplying it with a good training set and a minimal one. In the first case, it produced excellent results, while in the second case it gave consistent performance.
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Thamaraiselvi, S., Srivathsan, R., Imayavendhan, J., Muthuregunathan, R., Siddharth, S. (2009). Combining Naive-Bayesian Classifier and Genetic Clustering for Effective Anomaly Based Intrusion Detection. In: Sakai, H., Chakraborty, M.K., Hassanien, A.E., Ślęzak, D., Zhu, W. (eds) Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. RSFDGrC 2009. Lecture Notes in Computer Science(), vol 5908. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10646-0_55
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DOI: https://doi.org/10.1007/978-3-642-10646-0_55
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