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Exploit prediction based on machine learning

机译:基于机器学习的漏洞利用预测

摘要

Generation of one or more models is caused based on selecting training data comprising a plurality of features including a prevalence feature for each vulnerability of a first plurality of vulnerabilities. The one or more models enable predicting whether an exploit will be developed for a vulnerability and/or whether the exploit will be used in an attack. The one or more models are applied to input data comprising the prevalence feature for each vulnerability of a second plurality of vulnerabilities. Based on the application of the one or more models to the input data, output data is received. The output data indicates a prediction of whether an exploit will be developed for each vulnerability of the second plurality. Additionally or alternatively, the output data indicates, for each vulnerability of the second plurality, a prediction of whether an exploit that has yet to be developed will be used in an attack.
机译:基于选择包含多个特征的训练数据(包括针对第一个多个漏洞的每个漏洞的普遍性特征)的训练数据,生成一个或多个模型。一个或多个模型可以预测是否会针对漏洞开发漏洞和/或是否会用于攻击。将一个或多个模型应用于包含第二个多个漏洞的每个漏洞的普遍性特征的输入数据。根据一个或多个模型对输入数据的应用,接收输出数据。输出数据指示是否会为第二个多个漏洞的每个漏洞开发漏洞的预测。此外,输出数据指示,对于第二个复数的每个漏洞,预测尚未开发的漏洞是否将用于攻击。

著录项

  • 公开/公告号US12079346B2;US2024012079346B2;US12079346B2;US12079346

    专利类型

  • 公开/公告日2024-09-03

    原文格式PDF

  • 申请/专利权人 KENNA SECURITY LLC;

    申请/专利号US17693502;US202200017693502;US202217693502A;US202217693502

  • 发明设计人

    申请日2022-03-14

  • 分类号G06F21/57;G06N20;

  • 国家

  • 入库时间 2024-12-26 18:12:37

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