  This paper deals with the identification of machines in a smart city
environment. The concept of machine biometrics is proposed in this work for the
first time, as a way to authenticate machine identities interacting with humans
in everyday life. This definition is imposed in modern years where autonomous
vehicles, social robots, etc. are considered active members of contemporary
societies. In this context, the case of car identification from the engine
behavioral biometrics is examined. For this purpose, 22 sound features were
extracted and their discrimination capabilities were tested in combination with
9 different machine learning classifiers, towards identifying 5 car
manufacturers. The experimental results revealed the ability of the proposed
biometrics to identify cars with high accuracy up to 98% for the case of the
Multilayer Perceptron (MLP) neural network model.
