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Open AccessArticle

A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

Internet Interdisciplinary Institute (IN3), IT, Multimedia and Telecommunications Department, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain
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Academic Editor: Rongxing Lu
Sensors 2016, 16(6), 868; https://doi.org/10.3390/s16060868
Received: 15 April 2016 / Revised: 2 June 2016 / Accepted: 3 June 2016 / Published: 13 June 2016
(This article belongs to the Special Issue Security and Privacy in Sensor Networks)
In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%. View Full-Text
Keywords: anomaly detection; information security; outlier detection; smart cities; support vector machines; wireless sensor networks anomaly detection; information security; outlier detection; smart cities; support vector machines; wireless sensor networks
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Garcia-Font, V.; Garrigues, C.; Rifà-Pous, H. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks. Sensors 2016, 16, 868.

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