I-Corps: Data-Driven Risk Assessments for Software Vulnerabilities

这一i-Corps项目的更广泛的影响/商业潜力是开发技术，通过补充现有的漏洞评估工具来解决软件更新过程中的低效问题，并允许从业人员更快地更新关键漏洞，以降低攻击风险。世界经济论坛将网络攻击列为十年来全球十大风险之一。这些事件的主要原因之一是对软件漏洞的利用。最近的一项调查显示，27%的参与组织因未打补丁的漏洞而被攻破。漏洞利用的影响往往可以通过臭名昭著的勒索软件运动来看到，这些运动影响了几个关键的基础设施部门。漏洞利用已从最初的财务问题转变为威胁环境、国家安全甚至人类生命的社会问题。因此，消除作为网络攻击者目标的软件漏洞是当务之急。政府和行业组织可以通过软件更新修复其企业网络中的漏洞来降低攻击风险。该i-Corps项目基于一项技术的开发，该技术专注于减轻漏洞利用的影响，漏洞利用是网络攻击的主要启用因素之一，可以通过软件更新来防止。该项目将提供新的工具，通过提供对漏洞的自动、数据驱动的风险评估，解决管理软件更新所涉及的业务挑战。这些工具使用机器学习来学习漏洞和攻击之间的历史关联。在部署期间，该技术识别新披露的漏洞，收集公开可用的信息，并使用机器学习模型计算与针对这些漏洞的利用和攻击风险相关的各种组件。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of technology that addresses inefficiencies in the software update processes by complementing existing vulnerability assessment tools and allowing practitioners to update critical vulnerabilities faster in order to reduce the risk of attacks. The World Economic Forum ranks cyberattacks among the top 10 global risks of the decade. One of the main causes for these incidents are exploits against software vulnerabilities. A recent survey revealed that 27% of participating organizations were breached due to unpatched vulnerabilities. The impact of vulnerability exploits can often be seen through infamous ransomware campaigns which have affected several critical infrastructure sectors. Vulnerability exploits have shifted from being primarily a financial concern, to being a societal issue threatening the environment, national security, and even human life. As a result, removing software vulnerabilities as a target of cyber attackers is an urgent necessity. The government and industrial organizations can mitigate the risk of attacks by remediating vulnerabilities within their enterprise networks through software updates.This I-Corps project is based on the development of a technology that focuses on mitigating the impact of vulnerability exploits, which are one of the principal enablers of cyberattacks, and can be prevented through software updates. This project will provide novel tools to address the operational challenges involved in managing software updates by offering automatic, data-driven risk assessments for vulnerabilities. These tools use machine learning to learn historical associations between vulnerabilities and attacks. During deployment, the technology identifies newly disclosed vulnerabilities, collects publicly available information, and uses the machine learning models to compute various components related to the risk of exploits and attacks against these vulnerabilities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.