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A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data

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Advances on Intelligent Computing and Data Science (ICACIn 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 179))

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Abstract

The Internet of Things (IoT) is a grid of interconnected pre-programmed electronic devices to provide intelligent services for daily life tasks. However, the security of such networks is a considerable obstacle to successful implementation. Therefore, developing intelligent security systems for IoT is the need of the hour. This study investigates the performances of different Ensemble Learning (EL) approaches applied for intrusion detection in the IoT sensors’ telemetry data. We compare the accuracy of various EL approaches in homogeneous and heterogeneous combinations using bagging, boosting, and stacking strategies. These EL approaches apply well-known Machine Learning (ML) models such as Decision Tree (DT), Naıve Bayes (NB), Random Forest (RF), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and linear Support Vector Machine (SVM). We evaluate and compare EL approaches for binary and multi-class classification tasks on the ToN-IoT Telemetry dataset for intrusion detection. The results show that stacking EL outperform stand-alone ML algorithms-based classifiers as well as bagging and boosting.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Quaid-I-Azam University, Islamabad, Pakistan

    Naila Naz, Muazzam A. Khan & Muhammad Almas Khan

  2. Hazara University, Mansehra, Pakistan

    Muhammad Asad Khan

  3. School of Computing, Edinburgh Napier University, Edinburgh, UK

    Sana Ullah Jan & Jawad Ahmad

  4. Research Centre for Intelligent Healthcare, Coventry University, Coventry, UK

    Syed Aziz Shah

  5. School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, UK

    Arshad

  6. James Watt School of Engineering, University of Glasgow, Glasgow, UK

    Qammer H. Abbasi

Authors
  1. Naila Naz
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  2. Muazzam A. Khan
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  3. Muhammad Asad Khan
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  4. Muhammad Almas Khan
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  5. Sana Ullah Jan
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  6. Syed Aziz Shah
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  7. Arshad
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  8. Qammer H. Abbasi
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  9. Jawad Ahmad
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Corresponding author

Correspondence to Naila Naz .

Editor information

Editors and Affiliations

  1. School of Computing and Digital Technology, Birmingham City University, Birmingham, UK

    Faisal Saeed

  2. Information Systems Department, School of Computing, Universiti Utara Malaysia (UUM), Sintok, Malaysia

    Fathey Mohammed

  3. Faculty of Sciences, University of Hassan II Casablanca, Casablanca, Morocco

    Errais Mohammed

  4. Department of Computing and Technology, Nottingham Trent University, Nottingham, UK

    Tawfik Al-Hadhrami

  5. College of Computer Science and Engineering, Taibah University, Medina, Saudi Arabia

    Mohammed Al-Sarem

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Naz, N. et al. (2023). A Comparison of Ensemble Learning for Intrusion Detection in Telemetry Data. In: Saeed, F., Mohammed, F., Mohammed, E., Al-Hadhrami, T., Al-Sarem, M. (eds) Advances on Intelligent Computing and Data Science. ICACIn 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 179. Springer, Cham. https://doi.org/10.1007/978-3-031-36258-3_40

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