  Risk assessment is an inevitable step in implementation of a cyber-defense
strategy. An important part of this assessment is to reason about the impact of
possible attacks. In this paper, we propose a framework for estimating the
impact of cyber-attacks in stochastic linear control systems. The framework can
be used to estimate the impact of denial of service, rerouting, sign
alternation, replay, false data injection, and bias injection attacks. For the
stealthiness constraint, we adopt the Kullback-Leibler divergence between
residual sequences during the attack. Two impact metrics are considered: (1)
The probability that some of the critical states leave a safety region; and (2)
The expected value of the infinity norm of the critical states. For the first
metric, we prove that the impact estimation problem can be reduced to a set of
convex optimization problems. Thus, the exact solution can be found
efficiently. For the second metric, we derive an efficient to calculate lower
bound. Finally, we demonstrate how the framework can be used for risk
assessment on an example.
