麻雀搜索算法改进LSSVM的网络入侵检测
Network Intrusion Detection Based On LSSVM Improved by Sparrow Search Algorithm
毛一鸣 1程艳艳1
作者信息
- 1. 郑州工业应用技术学院 信息工程学院,河南 郑州 451100
- 折叠
摘要
针对最小二乘支持向量机模型进行网络入侵检测的性能受其控制参数设定的影响,为提高网络入侵检测的精度,提出一种基于麻雀搜索算法优化 LSSVM 模型控制参数的网络入侵检测模型.与 PSO-LSSVM 模型、GA-LSSVM 模型、GWO-LSSVM 模型和 LSSVM 模型相比,SSA-LSSVM模型的网络入侵检测精度最高,可以实现网络入侵的高精度检测,为网络安全维护和增强入侵检测功能提供科学参考.
Abstract
The performance of the Least Squares Support Vector Machine model in network intrusion detection is directly affected by the penalization parameters,so a network intrusion de-tection model based on LSSVM model is improved by sparrow search algorithm for higher accura-cy.Compared with PSO-LSSVM model,GA-LSSVM model,GWO-LSSVM model and LSSVM model,the SSA-LSSVM model has the highest accuracy of network intrusion detection.SSA-LSSVM can achieve high-precision network intrusion detection,and provide decision-making ba-sis for network security maintenance and enhance intrusion detection function.
关键词
入侵检测/麻雀搜索算法/最小二乘支持向量机/召回率/精确率Key words
intrusion detection/sparrow search algorithm/least squares support vector ma-chine/recall rate/accuracy rate引用本文复制引用
基金项目
2022年教育部高等教育司产学合作协同育人资助项目(220505115250522)
河南省高等教育教学改革研究与实践项目(2021SJGLX616)
河南省大中专院校就业创业课题(JYB2023094)
出版年
2024
NETL
NSTL
万方数据