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Modelling cybersecurity regulations for automated vehicles.

Shah Khalid Khan1, Nirajan Shiwakoti1, Peter Stasinopoulos1

  • 1School of Engineering, RMIT University Melbourne, Australia.

Accident; Analysis and Prevention
|April 6, 2023
PubMed
Summary
This summary is machine-generated.

Developing a dynamic model for Connected and Automated Vehicles (CAVs) cybersecurity regulations reveals key leverage points for stakeholders. Focusing on automaker innovation, risk-sharing, and data utilization is crucial for minimizing cyber-risks in CAVs.

Keywords:
Cyber-attacksCyber-physical systemDriverless carsGovernanceLaws

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Area of Science:

  • Cybersecurity
  • Systems Theory
  • Automotive Engineering

Background:

  • Connected and Automated Vehicles (CAVs) present new technical, legal, and social challenges.
  • Deterring criminal behavior in physical and cyber realms is a critical concern for CAVs.
  • Existing literature lacks a systematic tool to analyze the impact of cybersecurity regulations on diverse stakeholders.

Purpose of the Study:

  • To develop a dynamic modeling tool using systems theory to analyze the indirect consequences of CAV cybersecurity regulations.
  • To identify leverage points for minimizing cyber-risks in the medium to long term.
  • To address the knowledge gap in analyzing dynamically interacting stakeholder impacts on CAV cybersecurity.

Main Methods:

  • Systems theory was employed to develop a dynamic modeling tool.
  • A System Dynamic based Stock-and-Flow-Model (SFM) was used to model the Cybersecurity Regulatory Framework (CRF).
  • The SFM is founded on five pillars: Cybersecurity Policy Stack, Hacker's Capability, Logfiles, CAV Adopters, and intelligence-assisted traffic police.

Main Results:

  • Decision-makers should prioritize establishing a CRF based on automaker innovation.
  • Risk-sharing is essential to address underinvestment and knowledge asymmetries in cybersecurity.
  • Capitalizing on CAV-generated data offers significant opportunities for enhancing operations and security.

Conclusions:

  • The Cybersecurity Regulatory Framework (CRF) is a collective property of all Intelligent Transportation Systems (ITS) stakeholders.
  • Integrating intelligence analysts and computer crime investigators strengthens traffic police capabilities.
  • A balanced approach is needed for CAV-CRF, considering data accessibility, regulatory thresholds, business investment, and consumer privacy.