基于改进麻雀搜索算法的特征选择在入侵检测中的应用蔡昊，李军华，周成（南昌航空大学江西省图像处理与模式识别重点实验室，南昌330063）[摘要]机器学习在入侵检测中发挥着至关重要的作用，特征选择作为机器学习的关键预处理步骤，受到广大研究者的关注。针对麻雀搜索算法寻优能力强但易陷入局部最优的问题，本文对特征编码、位置更新等策略进行改进，提出一种多策略融合的二进制麻雀搜索算法，结合决策树分类器构造封装式特征选择算法，从高维特征空间中选择具有代表性的特征，以提高模型的预测能力并降低时间成本。基于NSL-KDD和UNSW-NB15数据集进行了性能评估，实验结果表明：与多种特征选择算法相比，利用该算法进行特征选择后的数据具有最佳二分类效果。[关键词]麻雀搜索算法；入侵检测；特征选择；特征编码；位置更新[中图分类号]TP393[文献标志码]Adoi：10.3969/j.issn.2096-8566.2023.02.009[文章编号]2096-8566(2023)02-0070-08ApplicationofFeatureSelectionBasedonImprovedSparrowSearchAlgorithminIntrusionDetectionCAIHao，LIJun-hua，ZHOUCheng（KeyLaboratoryofJiangxiProvinceforImageProcessingandPatternRecognition,NanchangHangkongUniversity,Nanchang330063,China）Abstract:Machinelearningplaysanimportantroleinintrusiondetection.Asakeypreprocessingstepofmachinelearning,featureselectionhasattractedtheattentionofmanyresearchers.Aimingatsolvingtheproblemthatsparrowsearchalgorithmhasstrongoptimizationabilitybuteasytofallintolocaloptimization,thisstudyimprovedthestrategiesoffeatureencodingandlocationupdate.Amultistrategymixedbinarysparrowsearchalgorithmwasproposed,whichcombinedadecisiontreeclassifiertoconstructwrappedfeatureselectionalgorithmandselectedrepresentativefeaturesfromhigh-dimensionalfeaturespacetoimprovethepredictionabilityofthemodelandreducethetimecost.TheperformanceofthisalgorithmwasevaluatedbasedonNSL-KDDandUNSW-NB15datasets.Theexperimentalresultsshowthatcomparedwithvariousfeatureselectionalgorithms,thedataafterfeatureselectionusingthealgorithmproposedinthisworkhasthebestbinaryclassificationeffect.Keywords:sparrowsearchalgorithm；intrusiondetection；featureselection；featureencoding；locationupdate引言随着互联网的高速发展...
