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Anomaly Detection using Machine Learning to Discover Sensor Tampering in IoT Systems
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-9596-2963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-3489-7429
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2021 (English)In: ICC 2021 - IEEE International Conference on Communications, IEEE, 2021Conference paper, Published paper (Refereed)
Abstract [en]

With the rapid growth of the Internet of Things (IoT) applications in smart regions/cities, for example, smart healthcare, smart homes/offices, there is an increase in security threats and risks. The IoT devices solve real-world problems by providing real-time connections, data and information. Besides this, the attackers can tamper with sensors, add or remove them physically or remotely. In this study, we address the IoT security sensor tampering issue in an office environment. We collect data from real-life settings and apply machine learning to detect sensor tampering using two methods. First, a real-time view of the traffic patterns is considered to train our isolation forest-based unsupervised machine learning method for anomaly detection. Second, based on traffic patterns, labels are created, and the decision tree supervised method is used, within our novel Anomaly Detection using Machine Learning (AD-ML) system. The accuracy of the two proposed models is presented. We found 84% with silhouette metric accuracy of isolation forest. Moreover, the result based on 10 cross-validations for decision trees on the supervised machine learning model returned the highest classification accuracy of 91.62% with the lowest false positive rate.

Place, publisher, year, edition, pages
IEEE, 2021.
Series
IEEE International Conference on Communications (ICC), E-ISSN 1938-1883
Keywords [en]
anomaly detection, Internet of Things, smart city, sensor tampering, traffic analysis, machine learning, security
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-83314DOI: 10.1109/ICC42927.2021.9500825ISI: 000719386003085Scopus ID: 2-s2.0-85115675450OAI: oai:DiVA.org:ltu-83314DiVA, id: diva2:1538372
Conference
IEEE International Conference on Communications, Montreal, Canada (Virtual), June 14-23, 2021
Note

ISBN för värdpublikation: 978-1-7281-7122-7

Available from: 2021-03-19 Created: 2021-03-19 Last updated: 2021-12-13Bibliographically approved

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Pathak, Aditya KumarSaguna, SagunaMitra, KaranÅhlund, Christer

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