# Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes

arXiv: https://arxiv.org/abs/2107.02897 · HTML: https://mlsanity.com/paper/2107.02897/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Is feature selection secure against training data poisoning?](https://mlsanity.com/paper/1804.07933/) (2018)
- [Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts](https://mlsanity.com/paper/2103.10594/) (2021)
- [A Taxonomy of Cyber Defence Strategies Against False Data Attacks in Smart Grid](https://mlsanity.com/paper/2103.16085/) (2021)

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

- [What Distributions are Robust to Indiscriminate Poisoning Attacks for Linear Learners?](https://mlsanity.com/paper/2307.01073/) (2023)

## Abstract (arXiv)

Accurate building energy prediction is useful in various applications starting from building energy automation and management to optimal storage control. However, vulnerabilities should be considered when designing building energy prediction models, as intelligent attackers can deliberately influence the model performance using sophisticated attack models. These may consequently degrade the prediction accuracy, which may affect the efficiency and performance of the building energy management systems. In this paper, we investigate the impact of bi-level poisoning attacks on regression models of energy usage obtained from household appliances. Furthermore, an effective countermeasure against the poisoning attacks on the prediction model is proposed in this paper. Attacks and defenses are evaluated on a benchmark dataset. Experimental results show that an intelligent cyber-attacker can poison the prediction model to manipulate the decision. However, our proposed solution successfully ensures defense against such poisoning attacks effectively compared to other benchmark techniques.