  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 scalability 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. We believe that device fingerprinting can be used to solve both
these problems effectively. In this work, we present a methodology to perform
device behavioral fingerprinting that can be employed to undertake device type
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
86-99% and a mean accuracy of 99%, across all our experiments. Our approach is
successful even when a device uses encrypted communication. Furthermore, we
show preliminary results for fingerprinting device categories, i.e.,
identifying different device types having similar functionality.
