Network intrusion detection based on Gradient Boosting Decision Tree(GBDT)has the problem that the unscientific hyper parameter selection of GBDT affects the model detection rate.Therefore,an intrusion detection model that integrates Improved Sparrow Search Algorithm(ISSA)to optimize GBDT is proposed.ISSA utilizes the good point set to initialize the population,enriching the diversity of the algorithm population.It incorporates a lens imaging reverse learning strategy to enhance the ability of the algorithm to escape local optimum.Additionally,the Whale Optimization Algorithm(WOA)is introduced to perturb the position of the sparrow individuals,providing the algorithm with stronger global search capability and higher convergence accuracy.The optimal hyper parameters of GBDT are obtained by ISSA optimization,and the ISSA-GBDT intrusion detection model is established.Experimental results show that the model has higher detection accuracy.
NETL
NSTL
万方数据
