南京师大学报（自然科学版）2025，Vol.48Issue(3)：73-83,11.DOI:10.3969/j.issn.1001-4616.2025.03.009
一种混合特征选择的朴素贝叶斯网络入侵检测算法
A Naive Bayes Network Intrusion Detection Algorithm with Mixed Feature Selection
摘要
Abstract
In intrusion detection applications,machine learning algorithms play a crucial role.Feature selection,as a key data preprocessing step,can effectively improve the classification performance of classifiers.However,existing feature selection algorithms do not consider the existence of pseudo-correlations between features when the data distribution is imbalanced,which affects the generalization ability of classifiers.To address this issue,a hybrid feature selection naive Bayes network intrusion detection algorithm is proposed,which introduces correlation measurement criteria into the feature extraction stage to avoid the pseudo-correlations between features and better satisfy the strong assumption of the naive Bayes algorithm,thereby improving the detection performance of the model.This method adopts a two-step feature selection strategy.In the first step,features that are strongly correlated with the class variable are selected from the dataset.In the second step,redundant features are removed to select a subset of mutually conditionally independent features,which are then fed into the naive Bayes algorithm for detection.Experimental results show that the proposed method outperforms 6 traditional machine learning algorithms in terms of detection rate and generalization performance,and it partially overcomes the problem of low accuracy caused by imbalanced data distribution.Compared with two recently proposed deep learning algorithms,it performs better in terms of accuracy and precision.关键词
网络入侵检测/条件独立性/特征选择/条件互信息/pearson相关系数Key words
network intrusion detection/conditional independence/feature selection/conditional mutual information/pearson correlation coefficient分类
计算机与自动化引用本文复制引用
郑锦波,王慧玲..一种混合特征选择的朴素贝叶斯网络入侵检测算法[J].南京师大学报（自然科学版）,2025,48(3):73-83,11.基金项目
新疆维吾尔自治区自然科学基金项目(2022D01C337)、伊犁师范学院重点学科开放课题(XJZDXKphy202302). （2022D01C337）
