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Design and Analysis for Fall Detection System Simplification
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Security-Related Hardware Cost Optimization for CAN FD-Based Automotive Cyber-Physical Systems.

Yong Xie1, Yili Guo2, Sheng Yang2

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.

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|October 26, 2021
PubMed
Summary

This study introduces two algorithms, SDH and IBH, to reduce hardware costs for securing automotive systems. These methods efficiently assign tasks and schedule messages, significantly cutting expenses for Hardware Security Modules (HSMs).

Keywords:
CAN FDautomotive cyber-physical systemscyber securitydesign space exploration algorithmhardware cost

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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.