CAREER: Going Beyond Linear Models for Attack Detection and Defense in Control Systems

职业：超越控制系统中攻击检测和防御的线性模型

• 批准号: 2143485
• 负责人: Justin Ruths
• 金额: $ 50.54万
• 依托单位: University of Texas at Dallas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2143485&HistoricalAwards=false
• 关键词: CAREER; Going; Beyond; Linear; Models

This Faculty Early Career Development (CAREER) project will contribute new knowledge related to the security of automation and control systems. Modern control systems enable automation across industries, from manufacturing to chemical processing, and regulate and monitor critical infrastructure, such as power, water, fuel, and transportation. However, these systems are susceptible to cyber-based attacks that target the computers that operate them. Cyber-based attacks could damage physical infrastructure, compromise safety, and harm the environment. Attack mitigation techniques rely on detecting discrepancies between the reported behavior of a physical process and the behavior predicted based on a model, or digital twin, of the process. However, existing models are often insufficient to capture the complex behavior of real-world applications. This award supports fundamental research for the development of detectors that aptly recognize, deter, and mitigate attacks. It will thus benefit the U.S. economy and society through increased resilience to cyber-attacks. Further, to enable a diversified and integrated workforce capable of meeting the security needs of the nation, this award will develop connections between university and community college students. It will support outreach activities and live demonstrations at local schools and community events to positively impact engineering education and to broaden participation of underrepresented groups in technical fields of study. The sensitivity of model-based detectors is intimately connected with the quality of the model used. To date, work has focused on linear dynamical models, which do not capture the complex behavior of real-world applications. To fill this gap, this award will develop a systematic framework for the security analysis of systems with uncertainty and of switched dynamical models. This research will mathematically characterize the relationship between different sources of uncertainty and detector sensitivity. It will parameterize the impact of a potential attack in terms of the system and detector uncertainty, establishing a vulnerability index based on system and uncertainty characteristics. The research team will act as both attacker and defender to develop new attack vectors that leverage the uncertainty present in systems that switch between different modes of operation.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.

这个教师早期职业发展（职业）项目将贡献与自动化和控制系统安全有关的新知识。现代控制系统使从制造业到化学处理的行业都可以自动化，并调节和监视关键基础设施，例如电力，水，燃料和运输。 但是，这些系统容易受到针对操作计算机的网络攻击的影响。基于网络的攻击可能会损害物理基础设施，损害安全性和损害环境。 攻击缓解技术依赖于检测物理过程报告行为与基于该​​过程的模型或数字双胞胎预测的行为之间的差异。但是，现有模型通常不足以捕获现实应用程序的复杂行为。该奖项支持基础研究，以发展恰当地认识，阻止和减轻攻击的探测器的发展。因此，它将通过增加对网络攻击的韧性来使美国经济和社会受益。 此外，为了使能够满足国家安全需求的多元化和综合的劳动力，该奖项将在大学和社区大学生之间建立联系。它将支持当地学校和社区活动的外展活动和现场演示，以积极影响工程教育，并扩大代表性不足的团体在技术领域的参与。基于模型的检测器的灵敏度与所使用模型的质量密切相关。 迄今为止，工作集中在线性动力学模型上，这些模型无法捕获现实世界应用的复杂行为。 为了填补这一空白，该奖项将为具有不确定性和切换动力学模型的系统的安全分析开发系统的框架。 这项研究将数学表征不同的不确定性来源与检测器敏感性之间的关系。 它将在系统和检测器不确定性方面参数化潜在攻击的影响，并根据系统和不确定性特征建立漏洞指数。 研究团队将既是攻击者又是防守者，以开发新的攻击向量，以利用系统切换不同操作模式的系统中存在的不确定性。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的审查标准通过评估来获得支持的。

项目成果

Co-Design for Resilience and Performance

弹性和性能的协同设计

• DOI: 10.1109/tcns.2022.3229774
• 发表时间: 2022
• 期刊: IEEE Transactions on Control of Network Systems
• 影响因子: 4.2
• 作者: Hashemi, Navid;Ruths, Justin
• 通讯作者: Ruths, Justin