Abstract: 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.

Key words: vulnerability assessment, light gradient boosting machine(LightGBM), evaluation model, quantum particle swarm optimization(QPSO), network assets