Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems

Area of Science:

• Automotive Engineering
• Cybersecurity
• Embedded Systems

Background:

• Automotive cyber-physical systems (ACPS) face security challenges due to increasing network integration.
• Hardware Security Modules (HSMs) are recommended for securing in-vehicle networks but increase hardware costs.
• Efficient solutions are needed to balance security needs with cost constraints in automotive ECUs.

Purpose of the Study:

• To propose two efficient design space exploration (DSE) algorithms, Stepwise Decreasing-based Heuristic (SDH) and Interference Balancing-based Heuristic (IBH).
• To minimize the number of required Hardware Security Modules (HSMs) for securing automotive systems.
• To reduce the overall hardware cost associated with security enhancements in multicore ECUs.

Main Methods:

• Developed two heuristic algorithms: Stepwise Decreasing-based Heuristic (SDH) and Interference Balancing-based Heuristic (IBH).
• Algorithms explore task assignment, task scheduling, and message scheduling to optimize HSM utilization.
• Evaluated algorithm performance using both synthetic and real-world automotive datasets.

Main Results:

• Both SDH and IBH outperform existing algorithms in minimizing HSM requirements.
• Significant hardware cost reductions observed: average 45.6% (SDH) and 61.4% (synthetic data) / 54.4% (SDH) and 64.3% (real data) for IBH.
• IBH generally performs better than SDH and offers runtime improvements of two to three orders of magnitude.

Conclusions:

• SDH and IBH effectively reduce hardware costs for securing automotive cyber-physical systems.
• The proposed algorithms provide a cost-efficient alternative to traditional HSM-based multicore ECU approaches.
• IBH demonstrates superior performance and efficiency, especially with a higher percentage of security-critical tasks.