  Vehicles are becoming more and more connected, this opens up a larger attack
surface which not only affects the passengers inside vehicles, but also people
around them. These vulnerabilities exist because modern systems are built on
the comparatively less secure and old CAN bus framework which lacks even basic
authentication. Since a new protocol can only help future vehicles and not
older vehicles, our approach tries to solve the issue as a data analytics
problem and use machine learning techniques to secure cars. We develop a Hidden
Markov Model to detect anomalous states from real data collected from vehicles.
Using this model, while a vehicle is in operation, we are able to detect and
issue alerts. Our model could be integrated as a plug-n-play device in all new
and old cars.

Artificial Intelligence;Cryptography and Security