Intrusion Detection System for Cyber-attacks in the Internet of Vehicles (IoV) Environment

This report presents the development of an Intrusion Detection System (IDS) designed to detect cyber-attacks in an Internet of Vehicles (IoV) environment. The IDS employs machine learning techniques, specifically Support Vector Machines (SVM), and is implemented on two low-memory, low-speed microcontroller systems connected to a CAN bus. The model is trained on real-world data, sourced from a publicly available dataset collected from a Hyundai Avante CN7, using Python-based tools on a computer. The resulting coefficients and biases are extracted and embedded into a C program, which runs on the microcontrollers. The final implementation achieved an accuracy of 83%, with a precision of 97% and a recall of 68%.