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

Author

Listed:
  • Mustain Billah

    (Department of CSE, Jashore University of Science and Technology (JUST), Jashore 7400, Bangladesh)

  • Adnan Anwar

    (Centre for Cyber Security Research and Innovation, Deakin University, Geelong 3217, Australia)

  • Ziaur Rahman

    (School of Computing Technologies, RMIT University, Melbourne 3001, Australia)

  • Syed Md. Galib

    (Department of CSE, Jashore University of Science and Technology (JUST), Jashore 7400, Bangladesh)

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.

Suggested Citation

  • Mustain Billah & Adnan Anwar & Ziaur Rahman & Syed Md. Galib, 2021. "Bi-Level Poisoning Attack Model and Countermeasure for Appliance Consumption Data of Smart Homes," Energies, MDPI, vol. 14(13), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3887-:d:583886
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    References listed on IDEAS

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    1. Kavousian, Amir & Rajagopal, Ram & Fischer, Martin, 2013. "Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior," Energy, Elsevier, vol. 55(C), pages 184-194.
    2. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
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