关键词: 朴素贝叶斯分类 入侵检测 信息增益

Abstract:

As for different network intrusion detections, their different actions have different data attributes. The traditional Naive Bayesian（NB） model of i ntrusion detection did not consider the difference. The paper exploits information gain in order to improve the traditional NB model,and use it to selec t features and delete unneeessary attributes in order to optimize NIK The experimental results show that information gain can optimize the traditional NB model to some extent, and have a higher detection rate for neural networks.

Key words: Naive Bayesian classifier, intrusion detectiom information gain