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

Energies

Adnan Anwar¹  Syed Md. Galib²  Mustain Billah²  Ziaur Rahman³ 

[1] Centre for Cyber Security Research and Innovation, Deakin University, Geelong 3217, Australia;Department of CSE, Jashore University of Science and Technology (JUST), Jashore 7400, Bangladesh;School of Computing Technologies, RMIT University, Melbourne 3001, Australia;

关键词: poisoning attack;    prediction model;    home appliances;    energy usage;    regression;   

DOI  :  10.3390/en14133887

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown