With the continuous development of computer technology and network technology, the scale of the network continues to expand, the network space tends to be complex, and the application of computers and networks has been deeply into politics, the military, finance, electricity, and other important fields. When security events do not occur, the vulnerability assessment of these high-risk network assets can be actively carried out to prepare for rainy days, to effectively reduce the loss caused by security events. Therefore, this paper proposes a multi-classification prediction model of network asset vulnerability based on quantum particle swarm algorithm-Lightweight Gradient Elevator (QPSO-LightGBM). In this model, based on using the Synthetic minority oversampling technique (SMOTE) to balance the data, quantum particle swarm optimization (QPSO) was used for automatic parameter optimization, and LightGBM was used for modeling. Realize multi-classification prediction of network asset vulnerability. 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 predictive performance indexes.

翻译：随着计算机技术与网络技术的持续发展，网络规模不断扩大，网络空间日趋复杂，计算机与网络的应用已深入政治、军事、金融、电力等重要领域。在安全事件未发生时，主动对这些高风险网络资产进行脆弱性评估可以防患于未然，有效降低安全事件造成的损失。为此，本文提出一种基于量子粒子群算法-轻量级梯度提升机（QPSO-LightGBM）的网络资产脆弱性多分类预测模型。该模型在使用合成少数类过采样技术（SMOTE）平衡数据的基础上，采用量子粒子群优化算法（QPSO）进行自动参数优化，并利用LightGBM进行建模，实现了网络资产脆弱性的多分类预测。为验证模型合理性，将所提模型与其他算法构建的模型进行了对比。结果表明，所提模型在各预测性能指标上均表现更优。