Publications by authors named "Carles Garrigues"
Article Synopsis
- Smart cities manage large amounts of data from various sources, requiring effective attack detection methods to prevent data breaches.
- This study assesses the effectiveness of two machine learning algorithms—support vector machines (SVM) and isolation forests—in detecting anomalies within a simulated smart city environment with diverse devices and systems.
- Findings indicate that while these machine learning techniques are valuable, their application in complex smart city scenarios presents significant challenges that need to be addressed for successful attack detection.
Urban areas around the world are populating their streets with wireless sensor networks (WSNs) in order to feed incipient smart city IT systems with metropolitan data. In the future smart cities, WSN technology will have a massive presence in the streets, and the operation of municipal services will be based to a great extent on data gathered with this technology. However, from an information security point of view, WSNs can have failures and can be the target of many different types of attacks.
View Article and Find Full Text PDFIn 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.
View Article and Find Full Text PDF