Behavioral Fingerprinting of IoT Devices

Abstract

The Internet-of-Things (IoT) has brought in new challenges in device identification --what the device is, and authentication --is the device the one it claims to be. Traditionally, the authentication problem is solved by means of a cryptographic protocol. However, the computational complexity of cryptographic protocols and/or problems related to key management, render almost all cryptography based authentication protocols impractical for IoT. The problem of device identification is, on the other hand, sadly neglected. Almost always an artificially created identity is softly associated with the device. We believe that device fingerprinting can be used to solve both these problems effectively. In this work, we present a methodology to perform IoT device behavioral fingerprinting that can be employed to undertake strong device identification. A device behavior is approximated using features extracted from the network traffic of the device. These features are used to train a machine learning model that can be used to detect similar device-types. We validate our approach using five-fold cross validation; we report a identification rate of 93-100 and a mean accuracy of 99%, across all our experiments. Furthermore, we show preliminary results for fingerprinting device categories, i.e., identifying different devices having similar functionality.