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

TL;DR

This paper explores the vulnerability of building energy prediction models to bi-level poisoning attacks and proposes an effective countermeasure, demonstrating its success on benchmark data to defend against malicious data manipulation.

Contribution

It introduces a novel bi-level poisoning attack model for energy prediction and proposes a new defense method that outperforms existing benchmarks.

Findings

Poisoning attacks can significantly degrade energy prediction accuracy.

The proposed countermeasure effectively defends against sophisticated poisoning attacks.

Experimental results validate the robustness of the proposed defense.

Abstract

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.