Secure and Resilient Cyber-Physical Systems

信息物理系统（CPS）是智能系统，包括物理和计算组件的工程交互网络。这些集成系统通过推动关键基础设施（CI）（如智能电网、石油和天然气炼油厂以及智能交通系统）的技术进步，提供了新的功能，以提高生活质量。然而，这些进步也带来了新的严峻挑战，首当其冲的是安全问题。安全性是ci体系结构的必要特性，可确保其功能不受恶意活动的损害。拟议的研究将开发人工智能（AI）辅助工具，用于智能状态监测、网络威胁狩猎和检测、威胁预测和威胁缓解，以保护加拿大的CIs。重点是；A)开发故障和异常检测的智能状态监测策略；B)开发最先进的多视图和多层元学习模型，用于准确的威胁搜索和检测；c)建立了一种新的基于随机博弈论的多智能体强化学习的攻击预测模型，以防止CI中的攻击传播和级联失败；D)开发深度模糊学习系统，收集和共享网络威胁情报，以适当减轻攻击和应对网络攻击。该研究与加拿大的网络安全战略以及CI国家战略和行动计划保持一致，以建立一个更安全，更安全，更有弹性的加拿大。

Cyber-physical systems (CPS) are smart systems that include engineered interacting networks of physical and computational components. These integrated systems provide new functionalities to improve quality of life by enabling technological advances in our Critical Infrastructure (CI) such as smart power grids, oil and gas refineries, and smart transportation systems. However, the advancements also lead to new and serious challenges, the primary one being security. Security is a necessary feature of the CIs architecture to ensure that their capabilities are not compromised by malicious activities. The proposed research will develop Artificial Intelligence (AI)-aided tools for intelligent condition monitoring, cyber threat hunting and detection, threat prediction, and threat mitigation to protect Canada's CIs. The focus is on; a) developing intelligent condition monitoring strategies for fault and anomaly detection; b) developing a state-of-the-art multi-view and multi-layer meta-learning enabled model for accurate threat hunting and detection; c) creating a novel stochastic game-theoretic multi-agent reinforcement learning-based attack prediction model to prevent attack propagation and cascading failure in CI; d) developing deep fuzzy learning systems to gather and share cyber threat intelligence for proper attack mitigation and response to cyber-attacks. The research aligns with Canada's Cyber Security Strategy and the National Strategy and Action Plan for CI to build a safer, more secure and more resilient Canada.