Using Data Analytics to Detect Anomalous States in Vehicles

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.

Cited by in corpus (5)

• A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN
• A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs
• Deep Learning-based Embedded Intrusion Detection System for Automotive CAN
• Exploring Highly Quantised Neural Networks for Intrusion Detection in Automotive CAN
• Real-Time Zero-Day Intrusion Detection System for Automotive Controller Area Network on FPGAs