Network intrusion detection algorithm based on LightGBM model and improved particle swarm optimization

In order to solve the problem of insufficient adaptive ability of the network intrusion detection model, the large-scale fast search capability of the particle swarm optimization (PSO) algorithm is introduced into the intrusion detection model. In order to solve the problem that PSO is easy to fall into local optimality, the genetic algorithm (GA) is introduced. An improved particle swarm optimization (GAPSO) algorithm based on genetic algorithm is proposed. This algorithm optimizes the parameters that are difficult to adjust in the lightweight gradient boosting machine (LightGBM) algorithm, so that the PSO algorithm can quickly converge while ensuring the optimization accuracy, and obtain the optimal network intrusion detection model. Experimental results show that GAPSO is more effective than the basic PSO algorithm when dealing with high-dimensional, complex structure optimization problems.