IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3887-d583886.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3887/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3887/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Małgorzata Sztorc, 2022. "The Implementation of the European Green Deal Strategy as a Challenge for Energy Management in the Face of the COVID-19 Pandemic," Energies, MDPI, vol. 15(7), pages 1-21, April.
    2. Guo, Peiyang & Lam, Jacqueline C.K. & Li, Victor O.K., 2019. "Drivers of domestic electricity users’ price responsiveness: A novel machine learning approach," Applied Energy, Elsevier, vol. 235(C), pages 900-913.
    3. Jaebin Lim & Myounggu Kang, 2022. "The relationship between site planning and electricity consumption: An empirical analysis of multi-unit residential complexes in Seoul, Korea," Environment and Planning B, , vol. 49(3), pages 971-986, March.
    4. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    5. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    6. Zhang, Mingyang & Zhang, Kaiwen & Hu, Wuyang & Zhu, Bangzhu & Wang, Ping & Wei, Yi-Ming, 2020. "Exploring the climatic impacts on residential electricity consumption in Jiangsu, China," Energy Policy, Elsevier, vol. 140(C).
    7. Wang, Xia & Fang, Yuan & Cai, Weiguang & Ding, Chao & Xie, Yupei, 2022. "Heating demand with heterogeneity in residential households in the hot summer and cold winter climate zone in China -A quantile regression approach," Energy, Elsevier, vol. 247(C).
    8. Boni Sena & Sheikh Ahmad Zaki & Hom Bahadur Rijal & Jorge Alfredo Ardila-Rey & Nelidya Md Yusoff & Fitri Yakub & Mohammad Kholid Ridwan & Firdaus Muhammad-Sukki, 2021. "Determinant Factors of Electricity Consumption for a Malaysian Household Based on a Field Survey," Sustainability, MDPI, vol. 13(2), pages 1-31, January.
    9. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    10. Kettani, Maryème & Sanin, Maria Eugenia, 2024. "Energy consumption and energy poverty in Morocco," Energy Policy, Elsevier, vol. 185(C).
    11. Fikru, Mahelet G., 2020. "Determinants of electricity bill savings for residential solar panel adopters in the U.S.: A multilevel modeling approach," Energy Policy, Elsevier, vol. 139(C).
    12. Chen, Chien-fei & Xu, Xiaojing & Adua, Lazarus & Briggs, Morgan & Nelson, Hannah, 2022. "Exploring the factors that influence energy use intensity across low-, middle-, and high-income households in the United States," Energy Policy, Elsevier, vol. 168(C).
    13. Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    14. Wallis, Hannah & Nachreiner, Malte & Matthies, Ellen, 2016. "Adolescents and electricity consumption; Investigating sociodemographic, economic, and behavioural influences on electricity consumption in households," Energy Policy, Elsevier, vol. 94(C), pages 224-234.
    15. Satre-Meloy, Aven, 2019. "Investigating structural and occupant drivers of annual residential electricity consumption using regularization in regression models," Energy, Elsevier, vol. 174(C), pages 148-168.
    16. Dimitra Kotsila & Persefoni Polychronidou, 2021. "Determinants of household electricity consumption in Greece: a statistical analysis," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-20, December.
    17. Nsangou, Jean Calvin & Kenfack, Joseph & Nzotcha, Urbain & Ngohe Ekam, Paul Salomon & Voufo, Joseph & Tamo, Thomas T., 2022. "Explaining household electricity consumption using quantile regression, decision tree and artificial neural network," Energy, Elsevier, vol. 250(C).
    18. Mayr, Kentaro Florian & Agnolucci, Paolo, 2024. "The impact of public health measures on household energy consumption: The case of the UK COVID-19 tier system," Energy Economics, Elsevier, vol. 138(C).
    19. Fikru, Mahelet G., 2019. "Estimated electricity bill savings for residential solar photovoltaic system owners: Are they accurate enough?," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    20. Calvin Nsangou, Jean & Kenfack, Joseph & Nzotcha, Urbain & Tamo, Thomas Tatietse, 2020. "Assessment of the potential for electricity savings in households in Cameroon: A stochastic frontier approach," Energy, Elsevier, vol. 211(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3887-:d:583886. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.