# Persuasion-based Robust Sensor Design Against Attackers with Unknown Control Objectives

## TL;DR

This paper develops a robust sensor design framework that uses persuasion strategies to influence attacker beliefs in stochastic control systems, minimizing damage from attackers with unknown objectives.

## Contribution

It introduces a linear-plus-noise signaling strategy and a semi-definite programming approach for robust sensor design against unknown attacker objectives in stochastic control.

## Key findings

- Closed-form solution for signaling strategy
- Linear matrix inequality condition for belief covariance
- Semi-definite program for global optimization

## Abstract

In this paper, we introduce a robust sensor design framework to provide "persuasion-based" defense in stochastic control systems against an unknown type attacker with a control objective exclusive to its type. For effective control, such an attacker's actions depend on its belief on the underlying state of the system. We design a robust "linear-plus-noise" signaling strategy to encode sensor outputs in order to shape the attacker's belief in a strategic way and correspondingly to persuade the attacker to take actions that lead to minimum damage with respect to the system's objective. The specific model we adopt is a Gauss-Markov process driven by a controller with a (partially) "unknown" malicious/benign control objective. We seek to defend against the worst possible distribution over control objectives in a robust way under the solution concept of Stackelberg equilibrium, where the sensor is the leader. We show that a necessary and sufficient condition on the covariance matrix of the posterior belief is a certain linear matrix inequality and we provide a closed-form solution for the associated signaling strategy. This enables us to formulate an equivalent tractable problem, indeed a semi-definite program, to compute the robust sensor design strategies "globally" even though the original optimization problem is non-convex and highly nonlinear. We also extend this result to scenarios where the sensor makes noisy or partial measurements. Finally, we analyze the ensuing performance numerically for various scenarios.