  The usage of technologically advanced devices has seen a boom in many
domains, including education, automation, and healthcare; with most of the
services requiring Internet connectivity. To secure a network, device
identification plays key role. In this paper, a device fingerprinting (DFP)
model, which is able to distinguish between Internet of Things (IoT) and
non-IoT devices, as well as uniquely identify individual devices, has been
proposed. Four statistical features have been extracted from the consecutive
five device-originated packets, to generate individual device fingerprints. The
method has been evaluated using the Random Forest (RF) classifier and different
datasets. Experimental results have shown that the proposed method achieves up
to 99.8% accuracy in distinguishing between IoT and non-IoT devices and over
97.6% in classifying individual devices. These signify that the proposed method
is useful in assisting operators in making their networks more secure and
robust to security breaches and unauthorized access.
