一种使用QPSO-LightGBM的网络资产脆弱性评估模型
A Model for Assessing Network Asset Vulnerability Using QPSO-LightGBM
AI总结：
针对网络资产脆弱性主动评估需求，提出结合SMOTE数据平衡、QPSO参数优化与LightGBM建模的多分类预测模型，实验验证其各项预测性能指标均优于对比算法模型。
摘要：
随着计算机技术与网络技术的不断发展，网络规模持续扩大，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。在安全事件尚未发生时，主动对这些高风险网络资产开展脆弱性评估，做到未雨绸缪，可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。该模型在采用合成少数类过采样技术（SMOTE）平衡数据的基础上，使用量子粒子群优化（QPSO）进行自动参数调优，再通过LightGBM建模，实现网络资产脆弱性的多分类预测。为验证模型的合理性，将所提模型与其他算法构建的模型进行对比，结果表明该模型在各项预测性能指标上表现更优。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
