改进麻雀搜索算法的入侵检测特征选择
Feature selection of intrusion detection based on improved sparrow search algorithm
刘涛 1蒙学强2
作者信息
- 1. 西安科技大学通信与信息工程学院,陕西西安 710600;西安科技大学西安市网络融合通信重点实验室,陕西西安 710600
- 2. 西安科技大学通信与信息工程学院,陕西西安 710600
- 折叠
摘要
针对网络入侵检测所处理数据存在特征维数高、检测效率低、准确率不高的问题,提出一种改进麻雀搜索算法的特征选择方法,旨在减少特征冗余的同时提高分类准确率.利用改进Circle映射初始化种群;结合秃鹰搜索算法中的螺旋搜索方式更新发现者位置;采用单纯形法和小孔成像法优化适应度较差和最优麻雀的位置,提升算法的寻优能力.将该算法与其它算法在6个经典基准函数上进行对比测试,其在收敛速度、精度等方面均有提升.使用数据集CIC-IDS2017进行特征选择实验,平均保留了 7.6个特征,准确率达到了 99.5％,结果表明,该算法可以在保证准确率的同时有效降低特征维度.
Abstract
Aiming at the problems of high feature dimension,low detection efficiency and low accuracy of the data processed by network intrusion detection,a feature selection method based on improved sparrow search algorithm was proposed to reduce fea-ture redundancy and improve classification accuracy.The improved Circle map was used to initialize the population.The location of the discoverer was updated by combining the spiral search method in the vulture search algorithm.The simplex method and small hole imaging method were used to optimize the location of the poor fitness and the optimal sparrow,to improve the optimi-zation ability of the algorithm.The algorithm was compared with other algorithms on six classical benchmark functions,and its convergence speed and accuracy were improved.The feature selection experiment was carried out using the dataset CIC-IDS2017,which retained an average of 7.6 features with an accuracy rate of 99.5％.The results show that the algorithm can effectively reduce feature dimensions while ensuring the accuracy.
关键词
麻雀搜索算法/Circle映射/螺旋搜索/单纯形法/小孔成像/入侵检测/特征选择Key words
sparrow search algorithm/Circle map/spiral search/simplex method/keyhole imaging/intrusion detection/feature selection引用本文复制引用
出版年
2024
NETL
NSTL
万方数据