Intrusion Detection Systems for In-Vehicle Networks: Protocols, Applications, and Challenges

In-vehicle networks (IVNs) connect numerous sensors, processors, and Electronic Control Units (ECUs) to enable real-time communication, but their growing complexity has amplified cybersecurity concerns. The Controller Area Network (CAN) bus, along with its variants CAN-FD, CAN-XL, and complementary protocols such as LIN and FlexRay, remains the backbone of IVNs. However, these protocols (the CAN specifically) were not originally designed to meet the stringent security demands of emerging autonomous and electric vehicles. This survey critically examines existing IVN security literature with a particular focus on intrusion detection systems (IDS) developed for CAN-based architectures. Unlike prior reviews that emphasize algorithmic design or attack taxonomy, this survey provides a comparison of IDS approaches (typically CAN-based due to its dominance in industry and research domain) based on physical-layer features, statistical parameters, information theory, and learning-based methods. We highlight unresolved security limitations of the CAN bus, synthesize mitigation strategies across industrial domains (automotive, aerospace, marine, agricultural, and battery systems), and identify overlooked challenges. Finally, we outline key research frontiers, such as hardware-assisted protection, edge intelligence, blockchainbased authentication, generative AI-driven anomaly detection, and quantum-safe cryptography—toward building resilient next-generation IVNs.