基于麻雀搜索算法和改进粒子群优化算法的网络入侵检测算法

针对网络入侵检测模型自适应能力不足的问题，将麻雀搜索算法（SSA）中的大范围快速搜索能力引入到粒子群优化（PSO）算法，提出基于麻雀搜索算法的改进粒子群优化（SSAPSO）算法。该算法通过对轻量级梯度提升机（LightGBM）算法中难以整定的参数进行寻优，使PSO算法在保证寻优精度的同时快速收敛，并得到最优的网络入侵检测模型。仿真实验结果表明，在4种基准函数上，SSAPSO比基本PSO算法收敛速度更快；在KDDCUP99数据集上，SSAPSO优化LightGBM后得到的SSAPSO-LightGBM算法比分类特征和梯度提升（CatBoost）算法的准确率、召回率、精确率和F1指数分别提升了15.12%、3.25%、21.26%和12.25%；SSAPSO-LightGBM算法在上述数据集中正常流量（Normal）、未授权远程访问（R2L）攻击、未授权本地访问（U2R）攻击、监听（PROBE）攻击的检测准确率比LightGBM算法分别提升了0.61%、3.14%、4.24%、1.04%和5.03%。

Network intrusion detection algorithm based on sparrow search algorithm and improved particle swarm optimization algorithm

Aiming at the problem of insufficient adaptive ability of network intrusion detection models， the large-scale fast search ability of Sparrow Search Algorithm （SSA） was introduced into Particle Swarm Optimization （PSO） algorithm， and a network intrusion detection algorithm based on Sparrow Search Algorithm and improved Particle Swarm Optimization Algorithm （SSAPSO） was proposed. In the algorithm， by optimizing the parameters that are difficult to set in Light Gradient Boosting Machine （LightGBM） algorithm， PSO algorithm converged quickly while ensuring the optimization accuracy， and an optimal network intrusion detection model was obtained. Simulation results show that on the four benchmark functions， SSAPSO converged faster than basic PSO algorithm. Compared with Categorical features+gradient Boosting （CatBoost） algorithm， SSAPSO optimized LightGBM （SSAPSO-LightGBM） has the accuracy， recall， precision and F1_score improved by 15.12%， 3.25%， 21.26% and 12.25% respectively on KDDCUP99 dataset. Compared with LightGBM algorithm， SSAPSO-LightGBM has the detection accuracy for Normal， Remote-to-Login （R2L） attack， User-to-Root （U2R） attack and Probeing （PROBE） attack on the above dataset improved by 0.61%， 3.14%， 4.24%， 1.04% and 5.03% respectively.