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

这个教师早期职业发展（Career）项目将贡献与自动化和控制系统安全相关的新知识。现代控制系统实现了从制造业到化学加工等各个行业的自动化，并调节和监控关键基础设施，如电力、水、燃料和运输。然而，这些系统很容易受到以操作它们的计算机为目标的网络攻击。基于网络的攻击可能破坏物理基础设施，危及安全，并危害环境。攻击缓解技术依赖于检测物理过程的报告行为与基于该过程的模型或数字孪生预测的行为之间的差异。然而，现有的模型通常不足以捕捉实际应用程序的复杂行为。该奖项支持用于适当识别、阻止和减轻攻击的检测器开发的基础研究。因此，它将通过增强对网络攻击的抵御能力，使美国经济和社会受益。此外，为了使多元化和一体化的劳动力能够满足国家的安全需求，该奖项将发展大学和社区学院学生之间的联系。它将支持在当地学校和社区活动进行外联活动和现场示范，以对工程教育产生积极影响，并扩大代表性不足的群体参与技术研究领域。基于模型的检测器的灵敏度与所用模型的质量密切相关。到目前为止，工作主要集中在线性动态模型上，它不能捕捉到现实世界应用程序的复杂行为。为了填补这一空白，该奖项将开发一个系统框架，用于不确定性系统和切换动态模型的安全分析。这项研究将在数学上描述不同不确定源和探测器灵敏度之间的关系。它将潜在攻击的影响以系统和检测器的不确定性参数化，建立基于系统和不确定性特征的脆弱性指数。研究团队将同时充当攻击者和防御者，以开发新的攻击媒介，利用在不同操作模式之间切换的系统中存在的不确定性。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.