Cyber-Physical Systems Security through Robust Adaptive Possibilitistic Algorithms: a Cross Layered Framework

该项目的目标是为智能电网开发一个跨层的网络-物理安全框架。提出的研究将通过异常分析提高智能电网实时监测的质量。这将为控制、第一反应人员的情况感知和智能电网的其他改进应用带来更可靠的数据。这项拟议的研究将通过开发确保安全可靠性能的大型复杂系统的分布式控制新技术，提高智能电网对仪表、参数、拓扑和通信基础设施以及大型物理干扰的网络攻击的恢复能力。该项目将通过在通信、机器学习、电力和控制系统之间架起桥梁，通过加强课程来促进教育。PI计划在会议上教授有关智能电网安全的短期课程。此外，他们还计划让未被充分代表的少数民族学生参与他们的项目。该项目旨在开发一种用于提高暂态稳定性的分布式非线性控制器。新的控制层将在分布式储能系统上运行，对建模中的不确定性具有鲁棒性，并能够补偿输入时滞，而不受运行条件的影响。此外，鲁棒控制器不需要系统动力学的精确知识。第二，将开发基于创新方法和分布式软件定义网络提供的跨层信息的不良数据分析。不良数据分析将考虑物理过程的内在相互依赖关系，同时提供对策。第三，将开发一种自适应分布式鲁棒机器学习方法。绝大多数有监督的机器学习方法需要大量仔细标记的代表数据分布的训练数据才能在测试中看到。然而，在安全应用中，新的威胁和恶意攻击不断被开发和尝试。因此，依赖先前训练数据的方法在以前从未见过的行为的情况下不太可能是稳健的，就像在快速变化的威胁环境中的情况一样。即将开发的新型分布式机器智能方法将专注于快速适应识别和区分新威胁，即使只有一个异常新威胁的例子。这一奖项反映了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 goal of this project is to develop a cross-layer cyber-physical security framework for the smart grid. The proposed research will improve the quality of real-time monitoring of the smart grid through anomaly analysis. This will lead to more reliable data for control, situation awareness to first responders and other improved applications to smart grids. The proposed research will improve the resilience of smart grids to cyber-attacks in meters, parameters, topology and communication infrastructure and large physical disturbances by developing new techniques for distributed control of large complex systems that guarantees secure and reliable performance. The project will foster education through enhancement to curriculum by building bridges among communications, machine learning, power and control systems. The PIs plan to teach short courses on smart grid security at conferences. In addition, they plan to engage under-represented minority students in their project. The project aims at developing a distributed nonlinear controller for transient stability enhancement. The new control layer will actuate on distributed energy storage systems, be robust to uncertainties in modelling and capable of compensating input time-delay while independent of operating conditions. Furthermore, the robust controller will not require exact knowledge of the system dynamics. Second, bad data analytics based on the innovation approach and cross-layered information provided by distributed software-defined network will be developed. The bad data analytics will consider the inherent interdependencies of the physical processes while providing a countermeasure. Third, an adaptive distributed robust machine learning approach will be developed. The overwhelming majority of supervised machine learning methods require large amounts of carefully labeled training data that is representative of the data distribution to be seen under test. However, in security applications, novel threats and malicious attacks are continuously being developed and attempted. Thus, approaches that rely on prior training data are unlikely to be robust in the case of behaviors never seen before, as would be the case in a rapidly changing threat environment. The novel distributed machine intelligence method that will be developed will be focused on being rapidly adaptive to identifying and distinguishing novel threats given even only one example of an anomalous novel threat.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.