Abstract
Intrusion detection can be considered as a classification task that attempts to classify a request to access network services as safe or malicious. Data mining techniques are being used to extract valuable information that can help in detecting intrusions. In this paper, we evaluate the performance of rule based classifiers like: JRip, RIDOR, NNge and Decision Table (DT) with Naïve Bayes (NB) along with their ensemble approach. We also propose to use the Semi-Naïve Bayesian approach (DTNB) that combines Naïve Bayes with the induction of Decision Tables in order to enhance the performance of an intrusion detection system. Experimental results show that the proposed approach is faster, reliable, and accurate with low false positive rates, which are the essential features of an efficient network intrusion detection system.
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Panda, M., Patra, M.R. (2009). Semi-Naïve Bayesian Method for Network Intrusion Detection System. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_70
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DOI: https://doi.org/10.1007/978-3-642-10677-4_70
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