IoT Device Authentication Using Self-Organizing Feature Map Data Sets

Sensors and actuators connected via the Internet of Things (loT) have now become embedded within our critical infrastructure offering improved observation and control as well as reduced costs. Given that software defined radios (SDRs) can be readily programmed to imitate loT devices, there is now a greater risk that assets can be spoofed or compromised. This necessitates an urgent need for loT device authentication, avoiding the need to upgrade the many thousands of individual devices. However, the lack of publicly available data sets severely hampers the development of effective authentication algorithms and mechanisms. In this regard, this article introduces a technique for facilitating loT device authentication when the radio frequency (RF) characteristics are highly correlated using self-organizing feature maps (SOFMs), thus aiming to promote state-of-the-art research in this field. The associated techniques demonstrated in this article exploit a novel data set of RF fingerprints and are, in particular, suitable for low-cost and long-range wireless application scenarios of the loT, for example, LoRa. Here, a well trained convolutional neural network (CNN) based on the SOFM data set can rapidly profile apparently correlated RF fingerprint patterns and thereby ascertain the nature of a specific device (friend or foe). In this way, a reliable and efficient loT device authentication strategy for LoRa devices can be established. The experimental results presented in this article substantiate the effectiveness and efficiency of the SOFM based approach, and the data sets are introduced in detail and shared with the research community.