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Anomaly Detection in Cellular IoT with Machine Learning

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Smart Objects and Technologies for Social Good (GOODTECHS 2021)

Abstract

The number of Internet of Things (IoT) devices used in eldercare are increasing day by day and bringing big security challenges especially for health care organizations, IoT service providers and most seriously for the elderly users. Attackers launch many attacks using compromised IoT devices such as Distributed Denial of Services (DDoS), among others. To detect and prevent these types of attacks on IoT devices connected to the cellular network, it is essential to have a proper overview of the existing threats and vulnerabilities. The main objective of this work is to present and compare different machine learning algorithms for anomaly detection in the cellular IoT scenario. Five supervised machine learning algorithms, namely KNN, Naïve Bayes, Decision Tree and Logistic Regression are used and evaluated by their performance. We see that, for both normal (using a local test dataset) and attack traffic (CICDDoS2019 (CICDDoS2019 Dataset: https://www.unb.ca/cic/datasets/ddos-2019.html.)) datasets, the accuracy and precision of the models are in average above 90%.

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Notes

  1. 1.

    Wireshark: www.wireshark.com.

  2. 2.

    Secure 5G4IoT Lab: https://5g4iot.vlab.cs.hioa.no/.

  3. 3.

    CICDDoS2019 Dataset: https://www.unb.ca/cic/datasets/ddos-2019.html.

  4. 4.

    Python: www.python.org.

  5. 5.

    Scikit-Learn: www.scikit-learn.org.

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Acknowledgement

This paper is a result of the H2020 Concordia project (https://www.concordia-h2020.eu) which has received funding from the EU H2020 programme under grant agreement No 830927. The CONCORDIA consortium includes 23 partners from industry and other organizations such as Telenor, Telefonica, Telecom Italia, Ericsson, Siemens, Airbus, etc. and 23 partners from academia such as CODE, university of Twente, OsloMet, etc.

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Authors and Affiliations

  1. Oslo Metropolitan Univeristy, Pilestredet 35, 0167, Oslo, Norway

    Bernardo Santos, Imran Qayyrm Khan, Bruno Dzogovic, Boning Feng, Van Thuan Do & Thanh Van Do

  2. Wolffia AS, Haugerudvn. 40, 0673, Oslo, Norway

    Van Thuan Do & Niels Jacot

  3. Telenor ASA, Snarøyveien 30, 1331, Fornebu, Norway

    Thanh Van Do

Authors
  1. Bernardo Santos
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  2. Imran Qayyrm Khan
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  3. Bruno Dzogovic
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  4. Boning Feng
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  5. Van Thuan Do
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  6. Niels Jacot
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  7. Thanh Van Do
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Corresponding author

Correspondence to Bernardo Santos .

Editor information

Editors and Affiliations

  1. Instituto de Telecomunicações, Universidade da Beira Interior, Covilhã, Portugal

    Ivan Miguel Pires

  2. Marche Polytechnic University, Ancona, Italy

    Susanna Spinsante

  3. Faculty of Computer Science and Engineering, Sts Cyril and Methodius University, Skopje, North Macedonia

    Eftim Zdravevski

  4. Faculty of Computer Science and Engineering, Sts Cyril and Methodius University, Skopje, North Macedonia

    Petre Lameski

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Santos, B. et al. (2021). Anomaly Detection in Cellular IoT with Machine Learning. In: Pires, I.M., Spinsante, S., Zdravevski, E., Lameski, P. (eds) Smart Objects and Technologies for Social Good. GOODTECHS 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 401. Springer, Cham. https://doi.org/10.1007/978-3-030-91421-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-91421-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91420-2

  • Online ISBN: 978-3-030-91421-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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