Using Data Analytics to Detect Anomalous States in Vehicles

TL;DR

This paper proposes a machine learning-based data analytics approach using Hidden Markov Models to detect anomalous states in vehicles, enhancing security across both new and old cars by real-time alerts.

Contribution

It introduces a novel application of Hidden Markov Models for vehicle security, enabling anomaly detection without requiring new protocols.

Findings

Effective detection of anomalous vehicle states in real-time

Applicable to both new and legacy vehicles with plug-and-play integration

Potential to improve vehicle cybersecurity significantly

Abstract

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.