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Anomaly Detection in Cellular IoT with Machine Learning

Santos, Bernardo; Khan, Imran Qayyrm; Dzogovic, Bruno; Feng, Boning; Do, Thuan Van; Jacot, Niels; Do, van Thanh
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ADML_paper_1.1.pdf (483.8Kb)
URI
https://hdl.handle.net/11250/2980706
Date
2021-01-01
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  • Publikasjoner fra Cristin [4189]
  • TKD - Institutt for informasjonsteknologi [1041]
Original version
https://doi.org/10.1007/978-3-030-91421-9_5
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 (CICDDoS20191) datasets, the accuracy and precision of the models are in average above 90%.
Publisher
Springer
Series
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;Volume 401
Journal
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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