基于QPSO-LightGBM网络资产脆弱性评估模型

Vulnerability Assessment Model of Network Asset Based on QPSO-LightGBM

为有效减少网络安全事件造成的损失,并对高风险网络资产进行漏洞评估,提出了一种基于量子粒子群轻量级梯度升降算法(QPSO-LightGBM:Quantum Particle Swarm Optimization-Light Gradient Boosting Machine)的多分类预测模型。该模型通过对少量过采样技术(MOTE:Minority Oversampling)进行合成从而达到数据平衡,采用量子粒子群算法(QPSO:Quantum Particle Swarm Optimization)实现参数的自动最优化,并使用LightGBM进行建模,进而实现网络资产的多分类预测。为验证模型的有效性,将所提模型与其他算法模型进行了比对,实验结果表明,该模型在各类预测性能指标上都取得了较好的效果。

With the increasing complexity of computer network space,in order to effectively reduce the losses caused by network security events,a multi classification prediction model based on the quantum particle swarm lightweight gradient descent algorithm(QPSO LightGBM:Quantum Particle Swarm Optimization-Light Gradient Boosting Machine)is proposed to evaluate vulnerabilities of high-risk network assets.Synthetic MOTE(Minority Oversampling)technique is used to balance the data,QPSO(Quantum Particle Swarm Optimization)is used for automatic parameter optimization is realized,and LightGBM is used for modeling.Multi-classification prediction of network asset vulnerability is realized.In order to verify the rationality of the model,the proposed model is compared with the model constructed by other algorithms.The results show that the proposed model is better in various performance indexes.