2 research outputs found
On the Computation of Worst Attacks: a LP Framework
We consider the problem of false data injection attacks modeled as additive
disturbances in various parts of a general LTI feedback system and derive
necessary and sufficient conditions for the existence of stealthy unbounded
attacks. We also consider the problem of characterizing the worst, bounded and
stealthy attacks. This problem involves a maximization of a convex function
subject to convex constraints, and hence, in principle, it is not easy to
solve. However, by employing a framework, we show how tractable
Linear Programming (LP) methods can be used to obtain the worst attack design.
Moreover, we provide a controller synthesis iterative method to minimize the
worst impact of such attacks
Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs
Consider a stochastic process being controlled across a communication
channel. The control signal that is transmitted across the control channel can
be replaced by a malicious attacker. The controller is allowed to implement any
arbitrary detection algorithm to detect if an attacker is present. This work
characterizes some fundamental limitations of when such an attack can be
detected, and quantifies the performance degradation that an attacker that
seeks to be undetected or stealthy can introduce