In the rapidly evolving landscape of automotive and agricultural technology, the security of in-vehicle networks (IVNs) has emerged as a critical concern. As vehicles become more connected and autonomous, the complexity of their internal networks grows, exposing them to potential cyber threats. A recent survey published in *IEEE Access* delves into the intricacies of intrusion detection systems (IDS) for IVNs, particularly those based on the Controller Area Network (CAN) bus, which remains the backbone of many modern vehicles and agricultural machinery.
The study, led by Junaid Ahmad Khan of Seoul National University of Science and Technology, critically examines existing IVN security literature, focusing on IDS developed for CAN-based architectures. Unlike previous reviews that emphasized algorithmic design or attack taxonomy, this survey compares IDS approaches based on physical-layer features, statistical parameters, information theory, and learning-based methods. This comprehensive analysis highlights the unresolved security limitations of the CAN bus and synthesizes mitigation strategies across various industrial domains, including automotive, aerospace, marine, agricultural, and battery systems.
One of the key challenges identified in the research is the original design of the CAN bus, which was not initially intended to meet the stringent security demands of emerging autonomous and electric vehicles. “The CAN bus, while robust and widely adopted, lacks inherent security features that are crucial for modern applications,” Khan explains. This gap has spurred the development of various IDS approaches, each with its own strengths and limitations.
For the agricultural sector, the implications are significant. Modern agricultural machinery increasingly relies on advanced IVNs to optimize performance, efficiency, and safety. However, as these systems become more interconnected, they also become more vulnerable to cyber threats. The research underscores the need for robust IDS to protect these networks from potential intrusions, ensuring the reliability and security of agricultural operations.
The survey also identifies several overlooked challenges and outlines key research frontiers that could shape the future of IVN security. These include hardware-assisted protection, edge intelligence, blockchain-based authentication, generative AI-driven anomaly detection, and quantum-safe cryptography. “Building resilient next-generation IVNs requires a multifaceted approach that integrates advanced technologies and innovative security strategies,” Khan notes.
As the agricultural industry continues to embrace smart technologies, the findings of this research could guide the development of more secure and reliable IVNs. By addressing the current limitations and exploring new security frontiers, the agricultural sector can enhance the safety and efficiency of its operations, ultimately contributing to a more sustainable and productive future.