计算机应用与软件2024，Vol.41Issue(11)：350-357,8.DOI:10.3969/j.issn.1000-386x.2024.11.048
基于混合二进制灰狼算法的入侵检测特征选择方法
INTRUSION DETECTION FEATURE SELECTING METHOD BASED ON HYBRID BINARY GREY WOLF OPTIMIZATION
摘要
Abstract
In order to reduce the negative impact of data set's redundant features on classifier's training speed and detection accuracy,which is used for intrusion detection,the binary gray wolf optimization(BGWO)mutation probability is analyzed and its mutation related vector's expression is reconstructed,improving BGWO's mutation mechanism,speeding up feature dimensionality reduction,and reducing classifier's training time.In addition,the iterative decision-making form of PSO was integrated,enhancing BGWO's optimization capabilities.Hybrid BGWO was adopted for wrapped feature selection,making data set's feature structure more suitable for the decision tree classifier.The NSL-KDD data set tests show that this method has good detection accuracy for DoS,Probe attack traffic,and is suitable for data sets with balanced data distribution.关键词
二进制灰狼算法/特征选择/入侵检测/决策树Key words
Binary grey wolf optimization/Feature selection/Intrusion detection system/Decision tree分类
信息技术与安全科学引用本文复制引用
胡琦渊,赵志衡,罗思婕,刘勇..基于混合二进制灰狼算法的入侵检测特征选择方法[J].计算机应用与软件,2024,41(11):350-357,8.基金项目
2020年工业互联网创新发展工程项目(TC200H037). （TC200H037）
