Towards Secure and Resilient Cyber-Physical Systems

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Cyber Physical Systems (CPSs) are the future of critical infrastructure such as transportation systems, electric grids, medical support, and major smart city applications driven by the Internet of Things (IoT). CPSs are interconnected, software-dependent systems with embedded sensors and actuators performing unconventional, complex interactions. Hence, they generally introduce an unexplored territory of security vulnerabilities and entail wider attack surfaces due to the physical processes involved and the deeply integrated cyber-physical space. Many CPS applications analyze the data collected from the physical world to generate critical decisions in real time. Consequently, erroneous or suboptimal decisions due to cyberattacks could have a disastrous impact on the CPS operations, its dependability, and safety. Furthermore, the prevention and detection of increasingly evolving cyber-physical attacks are not always successful. Therefore, CPSs should be designed with built-in tolerance to intrusions to guarantee an acceptable level of reliable performance under potential compromise. This research aims to design secure and resilient CPSs against stealthy attacks that target the perception, network, and application layers. It will address the three fundamental facets of computer and information security (i.e., intrusion prevention, detection, and mitigation) while respecting the intrinsic features (e.g., resource-constrained) and functional requirements (e.g., large scale) of CPSs. The proposed program is highly multidisciplinary in nature and encompasses three main tracks. The first track consists in developing smart intrusion prevention techniques as the primary line of defence, with special focus on the design of efficient authentication and authorization schemes, tailored to specific CPS environments such as connected vehicles and IoT. The second track is directed towards building robust, context-aware intrusion detection systems in highly dynamic CPSs by leveraging the power of deep learning techniques to enable more accurate estimation of abnormal system states and the detection of zero-day attacks. The third track involves the design of a new fabric of multi-layer, adaptive resilience which deeply integrates a wide range of advanced attack mitigation solutions, intelligent node collaboration approaches, and optimized incident recovery measures into the CPS operations and decision making at runtime. Together, the three tracks will create an original, defense-in-depth security strategy, with predominant focus on the initial design phases of secure and dependable cyber-physical software. The strategy will yield tremendous impact on current CPS design and implementation choices in the Canadian industry as well as nationwide critical infrastructures. The outcomes of the program will equip the research community with innovative security mindsets and tools that are essential to the radical advancement of the security status of emerging CPSs.