Simulation of Message Injection Attacks on Control Area Networks

The automobile industry is gradually incorporating new technologies into vehicles, making them increasingly connected, autonomous, and data-driven. It makes use of Control Area Network (CAN), a vital communication protocol for networking multiple Electronic Control Units (ECUs) within automobiles. Given its critical role in facilitating communication, CAN is vulnerable to a variety of cyber security threats. The objective of this work is to analyze the security elements of the CAN protocol while simulating attacks on CAN communication data to identify attack vectors targeting the CAN communication protocol and to preserve vehicle security. The proposed method involves analyzing and decoding CAN bus communication messages, which will aid in diagnostics, debugging, and security assessments by revealing insights into vehicle communication and behavior. This technique enables the vehicle to be secured with the required security measures, by investigating attack surfaces and attack vectors. As an effect, existing Python and CAN tools are being utilized to simulate message injection attacks and decode CAN messages in order to identify vulnerabilities, assess risks, and implement appropriate security solutions. The findings show that message injection simulation is carried out using Python and CAN tools, and CAN bus communication messages are captured. This results in disruption of the normal functioning of the vehicle. The captured data is inspected to decode and identify the components of the vehicle. Finally, an overview of best practices for countering cyber-attacks is provided for the security of in-vehicle network communication.