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Optimal Feature Set Selection for IoT Device Fingerprinting on Edge Infrastructure using Machine Intelligence

The widespread deployment of IoT devices globally has spurred the need for protecting these devices from various cyber security attacks. IoT device fingerprinting is the method of identifying IoT devices by analyzing network traffic. Fingerprinting helps to determine if devices have been subverted by an attacker by separating anomalous behavior from normal behavior. This kind of device identification is carried out on some edge device with the help of machine learning. With the growing number of IoT devices and the large volumes of traffic generated by them, it is essential to determine an optimal feature set for device classification. An optimal feature set not only helps to identify the most important features useful in creating a device fingerprint, but also makes the classification model light weight by eliminating the redundant ones and suitable for deployment on edge devices with low computational power. In this paper, we present a feature reduction method using three popular machine learning techniques and apply it for classifying IoT devices. Our feature reduction method identifies the most important features by isolating them from the non-essential ones, thereby giving a reduced feature set that can provide a classification accuracy comparable to the original feature vector. Moreover, we use the reduced feature set thus obtained to identify new IoT devices introduced in the network. We have performed experiments using an open source IoT device dataset. The experimental results show that we are able to identify the optimal features that constitute only 19% of the original feature vector. Moreover, the absence of 81% of the initial features does not compromise the performances of device classification as well as new device identification.