哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(2)：163-170,8.
改进杂交水稻优化算法的入侵检测特征选择
Feature selection for intrusion detection based on improved hybrid rice optimization algorithm
范晶晶 1于瓅1
作者信息
- 1. 安徽理工大学 计算机科学与工程学院,淮南 232001
- 折叠
摘要
Abstract
To address the issues of low detection efficiency and insufficient accuracy caused by feature redundancy in high-dimensional data for network intrusion detection,an improved hybrid rice optimization(HRO)algorithm was proposed specifically for feature selection.The population initialization was enhanced by employing an improved Circle chaotic map to increase the quality of the initial solutions.A horizontal and vertical crossover strategy was incorporated during the optimization process to augment population diversity.A Gaussian random walk mechanism was introduced to prevent the algorithm from becoming trapped in local optima.To evaluate the effectiveness of this method,experiments were conducted on UCI benchmark datasets and the NSL-KDD network intrusion detection dataset,validated using three classifiers:KNN,DT,and XGBoost.The experimental results demonstrated that on eight UCI datasets,the improved algorithm reduced the total number of original features to approximately 45%,and its classification performance outperformed the original HRO algorithm in all cases.On the NSL-KDD dataset,the enhanced algorithm reduced the original features to about 41%,achieving a maximum classification accuracy of 85.79%with the selected feature subset.关键词
入侵检测/特征选择/杂交水稻优化算法/Circle混沌映射/纵横交叉/高斯随机游走Key words
intrusion detection/feature selection/hybrid rice optimization algorithm/circle Chaotic mapping/crisscross optimization/Gaussian random walk分类
信息技术与安全科学引用本文复制引用
范晶晶,于瓅..改进杂交水稻优化算法的入侵检测特征选择[J].哈尔滨商业大学学报（自然科学版）,2026,42(2):163-170,8.
