Current learning algorithms cannot be easily applied in CPS, due to their need for continuous and expensive updates, with the current triggered frameworks having fundamental limitations. Such limitations lead to the following questions. How can we incorporate and fully adapt to totally unknown, dynamic, and uncertain environments? How do we co-design the action and the intermittent schemes? How can we provide quantifiable real-time performance, stability and robustness guarantees by design? And how do we solve congestion and guarantee security? We will build on our multiyear experience and
The Daniel Guggenheim School supports the Multidisciplinary Research Area of cyber-physical systems (CPS) - the interacting digital, analog, physical, and human components engineered for function through integrated physics and logic.
There has been an increasing demand for secure methods that can guarantee the integrity, safety, and normal operation of CPS, especially through attack detection and mitigation. Faculty research in this area focuses on a number of different applications, including the development of novel frameworks that will allow fully autonomous operation in the face of unknown, bandwidth restricted, and adversarial environments.
Incorporating intelligence in CPS makes the physical components more exposed to adversaries that can potentially cause failure or malfunction. Examples include the German steel mill attack, where the plant network of a steel facility was hacked and physically damaged, the 2015 Ukrainian blackout, where malicious actors managed to temporarily halt the supply of electricity from a few energy companies, and the Saudi Aramco incident where a virus caused disruption to the operation of an oil producer.