# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 2 Background and Related Work
### Cybersecurity Hazards of In-Vehicle Ethernet  
#### 2.2.1 Intrusion Detection Solutions  
In 2013, the BMW X5 became the first vehicle equipped with an in-vehicle Ethernet bus, marking the integration of Ethernet into automotive networks for the first time. Since then, Ethernet has seen increasing utilization in in-vehicle bus networks, with projections indicating it will be utilized in over 80% of vehicles by 2025 [13]. However, as connected car technologies continue to advance, Internet-connected vehicles require external connectivity through open interfaces, thereby escalating the risk of car attacks.  
Attackers may maliciously inject erroneous messages (e.g., braking commands) or disrupt the transmission of legitimate messages (e.g., tampering with, delaying, or deleting messages, relaying messages, etc.). They may also exploit vulnerabilities via the on-board diagnostic (OBD) interface or wireless connections, employing reverse engineering techniques to decipher the characteristics of existing nodes. Such actions pose serious threats to vehicle security, potentially endangering the driver's life and public safety [14]. Therefore, safeguarding vehicle networks is paramount.  
Vehicle manufacturers and automotive network providers must implement a range of measures to shield vehicles from cyberattacks. Intrusion detection solutions have been explored to address these threats:  
* [PERSON] et al. conducted an experiment to remotely and wirelessly attack a vehicle using a malicious smartphone and devised a security protocol for this purpose. The protocol adheres to the CAN bus specification and has been experimentally validated for  
Figure 2: Ethernet frame format.  
feasibility, demonstrating improvements in communication load and authentication delay compared to existing protocols [15].
* [PERSON] et al. introduced a lightweight intrusion detection algorithm based on CAN message interval analysis, capable of detecting all attack messages without triggering false alarms [16].
* [PERSON] et al. analyzed traffic characteristics of vehicular Ethernet networks and developed a lightweight anomaly detection system, employing machine learning techniques to identify anomalous traffic in vehicular environments, ensuring real-time performance and bolstering vehicle security [17].
* [PERSON] et al. scrutinized 13 instances of automotive hacking based on cyber kill chains, introducing an analysis methodology grounded in cyber kill chain attacks. They devised a vulnerability analysis system and recommended corresponding defensive measures [18].
* [PERSON] developed FFS-IDS, an advanced IDS for in-vehicle networks. It combined feature fusion with stacking ensemble learning to improve detection accuracy. FFS-IDS outperformed existing methods in identifying various attacks, achieving up to 99% accuracy for certain threats [19].
* [PERSON] developed an IDS for autonomous vehicles using K-nearest neighbors and ensemble learning. The system achieved up to 99% accuracy and improved F1-scores on real-world datasets, enhancing vehicle safety and cybersecurity [20].