AUTOMOTIVE ECUS CRYPTOJACKING: SIMULATION, DETECTION AND MITIGATION IN A VIRTUALIZED ENVIRONMENT

Abstract:

Cryptojacking is a covert cyberattack that mines Bitcoin without authorization by using computer resources.  It poses a serious risk to Electronic Control Units (ECUs) for automobiles, and they remain underexplored. Due to their susceptibility to specific automotive attacks as firmware tampering, remote code execution (RCE), and others, ECUs may be compromised via network-based exploits or over-the-air (OTA) updates. Attackers can use cryptojacking malware on ECUs once they have gained access to them, which can cause system slowdown, higher power usage, and safety hazards for vehicles.

The study showcases running cryptojacking malware (XMRig), keeping an eye on system resource usage, implementing a machine learning-based detection system, and utilizing blockchain technology to securely register events. Our solution sends email alerts, after identifying cryptojacking, and uses blockchain to for transparent logging.

 The outcomes show anomaly detection, secure logging, and real-time monitoring. Our work gives a scalable method for examining new threats in automotive cybersecurity research.