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

arXiv: https://arxiv.org/abs/1704.00748 · HTML: https://mlsanity.com/paper/1704.00748/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 4 of 4)

- [Deceptive Exploration in Multi-armed Bandits](https://mlsanity.com/paper/2510.08794/) (2025)
- [A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks](https://mlsanity.com/paper/2102.00573/) (2021)
- [Fundamental Limits of Man-in-the-Middle Attack Detection in Model-Free Reinforcement Learning](https://mlsanity.com/paper/2603.27592/) (2026)
- [Dynamical low-rank equilibrium computation for stochastic games between advanced persistent threats and moving target defense](https://mlsanity.com/paper/2610.06885/) (2026)

## Abstract (arXiv)

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.