# Data-Driven Control and Data-Poisoning attacks in Buildings: the KTH Live-In Lab case study

arXiv: https://arxiv.org/abs/2103.06208 · HTML: https://mlsanity.com/paper/2103.06208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems](https://mlsanity.com/paper/2211.08804/) (2022)

## Abstract (arXiv)

This work investigates the feasibility of using input-output data-driven control techniques for building control and their susceptibility to data-poisoning techniques. The analysis is performed on a digital replica of the KTH Livein Lab, a non-linear validated model representing one of the KTH Live-in Lab building testbeds. This work is motivated by recent trends showing a surge of interest in using data-based techniques to control cyber-physical systems. We also analyze the susceptibility of these controllers to data-poisoning methods, a particular type of machine learning threat geared towards finding imperceptible attacks that can undermine the performance of the system under consideration. We consider the Virtual Reference Feedback Tuning (VRFT), a popular data-driven control technique, and show its performance on the KTH Live-In Lab digital replica. We then demonstrate how poisoning attacks can be crafted and illustrate the impact of such attacks. Numerical experiments reveal the feasibility of using data-driven control methods for finding efficient control laws. However, a subtle change in the datasets can significantly deteriorate the performance of VRFT.