Communication and Security in Cyber-Physical Systems

This poster elaborates the results of two papers: "Event-triggered stabilization over digital channels of linear systems with disturbances," and "Authentication of cyber-physical systems under learning-based attacks."

In the first part of the poster, we study event-triggered control for stabilization of unstable linear plants over rate-limited communication channels subject to unknown, bounded delay and in the presence of bounded plant disturbances. In the same way that subsequent pauses in spoken language are used to convey information, it is also possible to transmit information in communication systems not only by message content (data payload) but also with its timing. We present an event-triggering strategy that utilizes timing information by transmitting in a state-dependent fashion. We show that for small values of the delay, the timing information implicit in the triggering events is enough to stabilize the plant with any positive information transmission rate. In contrast, when the delay increases beyond a critical threshold, the timing information alone is not enough to stabilize the plant and the data payload transmission rate begins to increase.

In the second part of the poster, the problem of attacking and authenticating cyber-physical systems is considered. Prior works assumed the system was known to all parties and developed watermark-based methods. In contrast, here the attacker needs to learn the open-loop gain in order to carry out a successful attack. A class of two-phase attacks is considered: during an exploration phase, the attacker passively eavesdrops and learns the plant dynamics, followed by an exploitation phase, during which the attacker hijacks the input to the plant and replaces the input to the controller with a carefully crafted fictitious sensor reading with the aim of destabilizing the plant without being detected by the controller. For an authentication test that examines the variance over a time window, tools from information theory and statistics are utilized to derive bounds on the detection and deception probabilities with and without a watermark signal, when the attacker uses an arbitrary learning algorithm to estimate the open-loop gain of the plant.