# Data-Injection Attacks in Stochastic Control Systems: Detectability and Performance Tradeoffs

## TL;DR

This paper investigates the fundamental limits and tradeoffs involved in detecting data-injection attacks in stochastic control systems, focusing on how stealthy attacks can degrade system performance while remaining undetected.

## Contribution

It characterizes the detectability limits and quantifies the performance tradeoffs for stealthy data-injection attacks in stochastic control systems.

## Key findings

- Identifies conditions under which attacks are detectable
- Quantifies the performance degradation caused by stealthy attacks
- Provides theoretical bounds on attack detectability

## Abstract

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.