期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2013
卷号：4
期号：6
页码：960-962
出版社：TechScience Publications
摘要：Classification is a classic data mining technique based on machine learning. Classification is used to classify each item in a set of data into one of predefined set of classes or groups. Naïve Bayes is a commonly used classification supervised learning method to predict class probability of belonging. This paper proposes a new method of Naïve Bayes Algorithm in which we tried to find effective detection rate and false positive rate of given data. We tested the performance of our proposed algorithm by employing KDD99 benchmark network intrusion detection dataset, and the experimental results proved that it improves detection rates as well as reduces false positives for different types of network intrusions.
