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Home / Archives / Volume-4 / Issue-2 / Article-1

Volume - 4 | Issue - 2 | june 2022

Intrusion Detection for Database Security using a Hidden Naïve Bayes Binary Classifier
M. Deepa  , J. Dhilipan
Pages: 48-57
Cite this article
Deepa, M. & Dhilipan, J. (2022). Intrusion Detection for Database Security using a Hidden Naïve Bayes Binary Classifier. Journal of Soft Computing Paradigm, 4(2), 48-57. doi:10.36548/jscp.2022.2.001
Published
30 May, 2022
Abstract

The Hidden Naive Bayes Binary Classifier is used for Database Security to detect Intruders. Data mining is used a lot in intrusion detection systems to classify normal or anomaly events. This method is a transparent, effective, and widely used mining method based on the idea of conditional attribute independence. HNB classifier is a more advanced version of Naive Bayes classifier algorithm and is efficiently used for intrusion attacks. It keeps the simplicity and efficiency of Naive Bayes, but loosens the independence condition. In the tests, it is proved that this binary classifier model can be used to solve the intrusion detection problem.

Keywords

Data mining intrusion detection machine learning classifier

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