一种智能化漏洞风险级别动态评估方法

An AI-Based Dynamic Vulnerability Assessment

网络安全所关注的重要内容之一就是漏洞的危害程度。目前已经有很多漏洞的评估算法,但是由于基于固化的公式计算处理手段,往往无法对漏洞的价值进行实时动态评估。通过分析当前漏洞评价方法存在的问题和不足,提出了基于双向LSTM和SVM的漏洞评分方法,包括数据预处理、特征选择、模型构建和实验验证。实验结果表明,基于BiLSTM-SVM的方法能够对漏洞进行准确的分类和预测,实现了对漏洞价值评价准确性的提高,为漏洞管理提供一种更加有效的动态评估方法。

Vulnerability assessment is a foundational and important work for network security.There are many existing vulnerability evaluation algorithms,but most of them are based on fixed formulas,so it is often unable to conduct real-time and dynamic evaluation on vulnerabilities.By analyzing the existing problems and deficiencies of the current vulnerability evaluation methods,this paper proposes the basic principle and algorithm flow based on bidirectional LSTM and SVM,including data pre-processing,feature selection,model construction,model construction and experimental verification.The experimental results show that BiLSTM-SVM can accurately classify and predict vulnerabilities,improve the accuracy and efficiency of vulnerability evaluation,and thus provide a powerful and more effective assessment method support for vulnerability management.