Abstract: With the development of the Internet， people enjoy the many conveniences it brings， but also face many threats， such as worms and Trojan horses. To defend against these malicious attacks， intrusion detection systems have been created. By detecting anomalies in the current network， intrusion detection systems can effectively detect attacks and take countermeasures. However， the accuracy of traditional machine learning algorithms in intrusion detection models is not high. Based on this， this paper proposes an intrusion detection model based on particle swarm optimization and LightGBM， specifically， an intrusion detection model is constructed by using the LightGBM method and a particle swarm algorithm is used to optimize the parameters of LightGBM. Experiments show that the method proposed in this paper can effectively improve the accuracy of the model， with 98.61% of accuracy， 98.25% of precision， 99.17% of recall rate and 98.70% of F1 score.

Key words: intrusion detection, particle swarm optimization, LightGBM, network security, decision tree