Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/151558
Title: Anomaly detection in smart city parking data: A case study
Author: Garcia-Font, Victor  
Garrigues, Carles  
Rifà-Pous, Helena  
Citation: Garcia-Font, V. [Victor], Garrigues, C. [Carles] & Rifà-Pous, H. [Helena]. (2016). Anomaly detection in smart city parking data: A case study. XIV Reunión Española sobre Criptología y Seguridad de la Información. p. 81-85.
Abstract: Wireless Sensor Networks (WSNs) are a principal technology in many smart city projects to collect data from the streets and send it to the data centers of the municipalities. Nonetheless, WSN are easy to attack and, therefore, smart city administrators have to verify that the WSN data is faultless. In this article we present a case study on anomaly detection in smart city parking data using WSN information from a Barcelona smart city provider. In order to analyze this data, we used Oneclass Support Vector Machines (OC-SVM) in a dataset with application information from the parking sensor readings and also system status data. The results of the anomaly analysis were satisfactory, achieving a 97.13% detection rate and 13.13% false positive rate.
Keywords: information security
intrusion detection
smart cities
support vector machines
wireless sensor networks
Document type: info:eu-repo/semantics/conferenceObject
Version: info:eu-repo/semantics/publishedVersion
Issue Date: 2-Oct-2016
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