CANon: Lightweight and Practical Cyber-Attack Detection for Automotive Controller Area Networks

Area of Science:

• Cybersecurity
• Automotive Engineering
• Network Security

Background:

• Automotive cyber-physical systems are shifting towards open-networking, increasing in-vehicle network vulnerabilities.
• Controller Area Network (CAN) is crucial for inter-vehicle communication but faces new security threats.
• Existing security measures may not adequately protect automotive networks, impacting safety.

Purpose of the Study:

• To propose a novel, lightweight, and practical cyber defense platform for automotive in-vehicle networks.
• To ensure real-time detection of cyber-attacks within automotive Controller Area Network (CAN) systems.
• To enhance automotive security and driver safety against emerging network threats.

Main Methods:

• Introduced CANon (CAN with origin authentication and non-repudiation), a hierarchical platform using centralized session management and distributed origin authentication.
• Employed sequential hash chains (SHC) for one-time local key generation and symmetric key-based detection for resource-limited nodes.
• Utilized keyed-message authentication codes (KMAC) for lightweight, group-based cyber-attack detection without impacting CAN performance.

Main Results:

• CANon achieved 100% detection rates for brute-force and replay attacks with a KMAC length over 16 bits.
• The platform demonstrated high security and real-time performance on low-power Electronic Control Units (ECUs).
• CANon operates as a software module without additional hardware and is compatible with CAN with Flexible Data rate (CAN-FD).

Conclusions:

• CANon provides a practical and effective solution for real-time cyber-attack detection in automotive networks.
• The lightweight design and efficient authentication mechanisms make CANon suitable for resource-constrained in-vehicle systems.
• The proposed platform significantly enhances the security and safety of automotive cyber-physical systems in open-networking environments.