Lilian K. Carvalho

TL;DR: This paper assumes that intermittent loss of observations may occur and proposes an automaton model based on a new language operation (language dilation) that takes it into account and presents a necessary and sufficient condition for robust diagnosability in terms of the language generated by the original automaton.

TL;DR: In this paper, the authors consider the problem of intrusion detection and mitigation in supervisory control systems, where the attacker has the ability to enable or disable vulnerable actuator commands and erase or insert vulnerable sensor readings.

TL;DR: A modular supervisor is built, which is the conjunction of two supervisors: the first one that enforces the robot to follow the path defined by a planner and the second one that guarantees the satisfaction of the specifications such as prevention of collisions and task and movement management.

TL;DR: A polynomial time verification algorithm is presented to verify robust diagnosability of a given fault event despite the possibility of permanent loss of observations and a methodology to perform online diagnosis is presented using a set of partial diagnosers.

TL;DR: A mathematical model for the system under certain classes of actuator enablement attacks, sensor erasure attacks, or sensor insertion attacks is presented and a defense strategy that aims to detect such attacks online and disables all controllable events after an attack is detected is proposed.