Computationally-Efficient Secured IoT Networks: Devices Fingerprinting using Low Cost Machine Learning Techniques

Abstract

The vulnerability of wireless devices to a well-known set of probable cyberattacks has made safeguarding the networks to which these devices connect a tremendous security issue, threatening the safety and security of thousands, if not millions, of private and public networks. Due to the rapid growth of embedded and wearable wireless devices on the market, wireless Internet of Things (IoT) devices are now one of the most vulnerable entry points because they don’t have advanced authentication procedures. This article provides a summary of our most recent findings in the development of a novel authentication and identification method for IoT ZigBee and Long Range (LoRa) devices based on the physical signals they emit. Our method relies on the extraction of a collection of unique features from the received modulated signal in order to construct a features vector for each device and then train a machine learning model using the acquired features. Following training, the trained model is evaluated by testing its ability to identify and recognize the authorized devices (i.e., those previously included in the training set) from the testing set, which contains an evenly distributed random mix of new and authorized devices. Our method employs differential constellation trace Figure (DCTF)-based features for the features vector and computationally-efficient machine learning methods, such as Quadratic Discriminant Analysis (QDA) and Gaussian Naive Bayes classifiers, which resulted in a recognition accuracy greater than 90 percent.