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LightGBM-RF: A Hybrid Model for Anomaly Detection in Smart Building

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Frontiers in Cyber Security (FCS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1726))

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Abstract

Smart building uses sophisticated and integrated building technology and allows numerous IoT systems to interact as well as provide convenience to its users. Unfortunately, smart buildings have become a point of attraction for cybercriminals. Due to the fact that the majority of these IoT devices lack the memory and computing power required for robust security operations, they are inherently vulnerable. IoT devices are consequently vulnerable to various attacks. Therefore, a single attack on network systems or devices can cause serious harm to the security of data as well as privacy in a smart building.

This paper presents LightGBM-RF, a machine learning model that accurately detects anomalies in a smart building by utilizing a combination of Light Gradient Boosting Machine and Random Forest algorithms. The model detects anomalies with an accuracy of 99.19%, thereby providing an effective scheme for detecting different attack families, and the potential to significantly improve security in smart buildings.

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Authors and Affiliations

  1. Department of Computer Science, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Otuekong Ekpo, Kate Takyi & Rose-Mary Owusuaa Mensah Gyening

Authors
  1. Otuekong Ekpo
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  2. Kate Takyi
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  3. Rose-Mary Owusuaa Mensah Gyening
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Correspondence to Otuekong Ekpo .

Editor information

Editors and Affiliations

  1. Kwame Nkrumah University of Science and Technology, Kumasi, Ghana

    Emmanuel Ahene

  2. University of Electronic Science and Technology of China, Chengdu, China

    Fagen Li

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Ekpo, O., Takyi, K., Gyening, RM.O.M. (2022). LightGBM-RF: A Hybrid Model for Anomaly Detection in Smart Building. In: Ahene, E., Li, F. (eds) Frontiers in Cyber Security. FCS 2022. Communications in Computer and Information Science, vol 1726. Springer, Singapore. https://doi.org/10.1007/978-981-19-8445-7_3

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  • DOI: https://doi.org/10.1007/978-981-19-8445-7_3

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  • Print ISBN: 978-981-19-8444-0

  • Online ISBN: 978-981-19-8445-7

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