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Repositori Institucional (O2) author Garcia-Font, Victor author Garrigues, Carles author Rifà-Pous, Helena other Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3) other Universitat Rovira i Virgili (URV) 2019-04-15T11:37:27Z 2019-04-15T11:37:27Z 2018-09-21 Garcia-Font, V., Garrigues, C., & Rifà-Pous, H. (2018). Difficulties and challenges of anomaly detection in smart cities: a laboratory analysis. Sensors, 18(10). doi:10.3390/s18103198 1424-8220 http://hdl.handle.net/10609/93232 10.3390/s18103198 Smart cities work with large volumes of data from sensor networks and other sources. To prevent data from being compromised by attacks or errors, smart city IT administrators need to apply attack detection techniques to evaluate possible incidents as quickly as possible. Machine learning has proven to be effective in many fields and, in the context of wireless sensor networks (WSNs), it has proven adequate to detect attacks. However, a smart city poses a much more complex scenario than a WSN, and it has to be evaluated whether these techniques are equally valid and effective. In this work, we evaluate two machine learning algorithms (support vector machines (SVM) and isolation forests) to detect anomalies in a laboratory that reproduces a real smart city use case with heterogeneous devices, algorithms, protocols, and network configurations. The experience has allowed us to show that, although these techniques are of great value for smart cities, additional considerations must be taken into account to effectively detect attacks. Thus, through this empiric analysis, we point out broader challenges and difficulties of using machine learning in this context, both for the technical complexity of the systems, and for the technical difficulty of configuring and implementing them in such environments. eng CC BY testbed wireless sensor networks isolation forest support vector machines smart cities outlier detection information security anomaly detection xarxa de sensors sense fils banc de proves bosc d'aïllament màquines de vectors suport ciutats intel·ligents detecció de outliers seguretat de la informació detecció d'anomalia red de sensores inalámbricos banco de pruebas bosque de aislamiento máquinas de vectores de soporte ciudades inteligentes detección de outliers seguridad de la información detección de anomalías Difficulties and challenges of anomaly detection in smart cities: a laboratory analysis info:eu-repo/semantics/article URL https://openaccess.uoc.edu/bitstreams/92c90d2e-facc-40af-86af-e7daf86360b3/download File MD5 4fcde690628936de92893e2860883054 9578733 application/pdf anomalydetection.pdf