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Internet-of-Things (IoT) devices are nowadays massively integrated in dailylife: homes, factories, or public places. This technology offers attractiveservices to improve the quality of life as well as new economic markets throughthe exploitation of the collected data. However, these connected objects havealso become attractive targets for attackers because their current securitydesign is often weak or flawed, as illustrated by several vulnerabilities suchas Mirai, Blueborne, etc. This paper presents a novel approach for detectingintrusions in smart spaces such as smarthomes, or smartfactories, that is basedon the monitoring and profiling of radio communications at the physical layerusing machine learning techniques. The approach is designed to be independentof the large and heterogeneous set of wireless communication protocolstypically implemented by connected objects such as WiFi, Bluetooth, Zigbee,Bluetooth-Low-Energy (BLE) or proprietary communication protocols. The mainconcepts of the proposed approach are presented together with an experimentalcase study illustrating its feasibility based on data collected during thedeployment of the intrusion detection approach in a smart home under real-lifeconditions.

RadIoT: Radio Communications Intrusion Detection for IoT - A Protocol Independent Approach