基于多门共享—私有混合专家的漏洞CVSS度量预测系统OA

To address the subjectivity and inefficiency of manual common vulnerability scoring system(CVSS)assessment and the limitations of existing automated methods in capturing inter-metric relation-ships,a CVSS metrics prediction model of vulnerability based on a multi-gate shared and private mix-ture of experts(MSPMoE)is proposed.In the model,a shared-private expert mechanism is used to em-ployed enyto jointly leverage shared and unique features across different metrics.Firstly,the Distil-BERT model is fine-tuned on a collected vulnerability description corpus as the feature encoder.Then,a dynamic feature extraction module is designed to adaptively select optimal feature representa-tion strategies.Finally,the MSPMoE classifier is adopted,where shared experts are used to capture common patterns across metrics,while private experts are used to extract metric-specific features.A gating network is used to dynamically weight each expert's contribution,to balance shared and private knowledge for accurate CVSS metrics prediction.Experiments show that the proposed model outper-forms state-of-the-art methods on seven CVSS metrics,achieving an average accuracy of 83.32%across all eight metrics.