Improvement of the type identification of connected objects using supervised machine learning

With the increasing number of connected objects and their growing variety, the identification of connected objects is key to help users to better manage them and to ensure security in their home networks. We deal with this problem using a supervised classification based on traffic flow attributes of these objects and on textual data carried by applicative layers. Our results show that our solution has a precision of 99% on average.