结合粒子群优化和LightGBM的入侵检测方法

Intrusion Detection Method Based on Particle Swarm Optimization Combined with LightGBM

随着互联网的发展,人们在享受互联网带来的诸多便利之外,同时也面临着许多威胁,如蠕虫、木马等。为了抵御上述恶意攻击,入侵检测系统应运而生。通过检测当前网络中的异常情况,入侵检测系统能有效检测各项攻击进而采取对应措施。然而,传统的机器学习算法在入侵检测模型中准确率并不高,为此,提出一种基于粒子群优化和LightGBM的入侵检测方法,使用LightGBM方法搭建入侵检测模型,采用粒子群算法优化LightGBM的参数。实验表明,本文提出的方法能够有效提升效果,准确率达98.61%、精确率达98.25%、召回率达99.17%、F1值达98.70%。

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.