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。该研究将通过异常分析提高智能电网的实时监测质量。这将导致更可靠的数据用于控制，第一响应者的情况感知以及智能电网的其他改进应用。拟议的研究将通过开发大型复杂系统分布式控制的新技术来提高智能电网对仪表，参数，拓扑结构和通信基础设施以及大型物理干扰的网络攻击的弹性，以保证安全可靠的性能。该项目将通过在通信、机器学习、电力和控制系统之间建立桥梁来加强课程，从而促进教育。PI计划在会议上教授关于智能电网安全的短期课程。 此外，他们计划让代表性不足的少数民族学生参与他们的项目。本计画旨在发展一种分散式非线性暂态稳定控制器。新的控制层将驱动分布式储能系统，对建模中的不确定性具有鲁棒性，并且能够在独立于操作条件的情况下补偿输入时延。此外，鲁棒控制器将不需要系统动态的精确知识。其次，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。坏数据分析将考虑物理过程的内在相互依赖性，同时提供对策。第三，将开发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都需要大量经过仔细标记的训练数据，这些数据代表了测试中要看到的数据分布。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，依赖于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鲁棒的，就像在快速变化的威胁环境中一样。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。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.