用于入侵检测特征选择的改进灰狼优化算法  

Improved grey wolf optimization algorithm for intrusion detection feature selection

作　　者：丁俊 韦文山[1] 鲍杰 DING Jun;WEI Wenshan;BAO Jie(School of Electronic Information,Guangxi Minzu University,Nanning 530006,China)

机构地区：[1]广西民族大学电子信息学院,广西南宁530006

出　　处：《微电子学与计算机》2024年第6期28-37,共10页Microelectronics & Computer

基　　金：国家自然科学基金地区科学基金(61762011)。

摘　　要：针对标准GWO收敛精度低、易早收敛等问题,提出了一种改进灰狼优化算法GPGWO。首先为了使狼群均匀分布在搜索空间中,结合拉丁超立方体抽样与反向学习来初始化种群位置;然后增加迭代计算过程中狼群的多样性,将灰狼分成不同类型个体,使用不同的位置更新策略;最后对α狼进行随机Levy飞行游走,迫使其离开原本位置。将GPGWO与3种改进GWO算法在广泛使用的10个基准函数上进行比较,仿真结果表明,GPGWO在寻优方面具有一定的优势。随后把GPGWO应用在入侵检测特征选择场景中,通过与不同的分类器相结合形成特征选择算法,实现对高维数据集的降维处理,通过对入侵检测数据集的实验证明,该算法能够保留最优的特征子集,仅用部分特征就能获得最佳的检测效果。Aiming at the problems of low convergence accuracy and easy early convergence of standard GWO,an improved grey wolf optimization algorithm GPGWO was proposed.Firstly,in order to evenly distribute wolves in the search space,the Latin hypercube sampling and reverse learning were combined to initialize the population position.Then,the diversity of wolves in the iterative calculation process was increased,and grey wolves were divided into different types of individuals and different position updating strategies were used.Finally,the alpha Wolf is forced to leave its original location by a random Levy flight.By comparing GPGWO with three improved GWO algorithms on 10 widely used benchmark functions,the simulation results show that GPGWO has certain advantages in optimization.Then,GPGWO is applied in the feature selection scenario of intrusion detection.By combining with different classifiers,GPGWO forms a feature selection algorithm to achieve dimension reduction processing of high-dimensional data sets.Through experiments on intrusion detection data sets,it is proved that the algorithm can retain the optimal feature subset and obtain the best detection effect with only part of the features.

关 键 词：入侵检测 灰狼算法 特征选择 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]