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Conceptualizing and Modeling Communication-Based Cyberattacks on Automated Vehicles

Tianyi Li, Tianyu Liu, Yicheng Yang

TL;DR

This work addresses cyber threats targeting ACC-enabled automated vehicles by formulating six communication-based attack vectors and evaluating their effects using a ring-road IDM-based microscopic simulation that varies ACC market penetration rates and compromised-vehicle patterns. Each attack is mathematically characterized, enabling a systematic pre/during/post analysis and a risk taxonomy that distinguishes low-, variable-, and high-risk scenarios. Key findings show that EV platoons generally exhibit lower velocity and spacing fluctuations and faster post-attack recovery than ICE platoons, but perception-based Blinding Attacks can negate these advantages and trigger collisions, while Mixed Attacks can unexpectedly reduce severity due to interaction effects. The results offer actionable guidance for detection and mitigation strategies in mixed-fleet traffic, informing practitioners and policymakers about where to prioritize security investments and how to design resilient ACC systems against diverse cyber threats.

Abstract

Adaptive Cruise Control (ACC) is rapidly proliferating across electric vehicles (EVs) and internal combustion engine (ICE) vehicles, enhancing traffic flow while simultaneously expanding the attack surface for communication-based cyberattacks. Because the two powertrains translate control inputs into motion differently, their cyber-resilience remains unquantified. Therefore, we formalize six novel message-level attack vectors and implement them in a ring-road simulation that systematically varies the ACC market penetration rates (MPRs) and the spatial pattern of compromised vehicles. A three-tier risk taxonomy converts disturbance metrics into actionable defense priorities for practitioners. Across all simulation scenarios, EV platoons exhibit lower velocity standard deviation, reduced spacing oscillations, and faster post-attack recovery compared to ICE counterparts, revealing an inherent stability advantage. These findings clarify how controller-to-powertrain coupling influences vulnerability and offer quantitative guidance for the detection and mitigation of attacks in mixed automated traffic.

Conceptualizing and Modeling Communication-Based Cyberattacks on Automated Vehicles

TL;DR

This work addresses cyber threats targeting ACC-enabled automated vehicles by formulating six communication-based attack vectors and evaluating their effects using a ring-road IDM-based microscopic simulation that varies ACC market penetration rates and compromised-vehicle patterns. Each attack is mathematically characterized, enabling a systematic pre/during/post analysis and a risk taxonomy that distinguishes low-, variable-, and high-risk scenarios. Key findings show that EV platoons generally exhibit lower velocity and spacing fluctuations and faster post-attack recovery than ICE platoons, but perception-based Blinding Attacks can negate these advantages and trigger collisions, while Mixed Attacks can unexpectedly reduce severity due to interaction effects. The results offer actionable guidance for detection and mitigation strategies in mixed-fleet traffic, informing practitioners and policymakers about where to prioritize security investments and how to design resilient ACC systems against diverse cyber threats.

Abstract

Adaptive Cruise Control (ACC) is rapidly proliferating across electric vehicles (EVs) and internal combustion engine (ICE) vehicles, enhancing traffic flow while simultaneously expanding the attack surface for communication-based cyberattacks. Because the two powertrains translate control inputs into motion differently, their cyber-resilience remains unquantified. Therefore, we formalize six novel message-level attack vectors and implement them in a ring-road simulation that systematically varies the ACC market penetration rates (MPRs) and the spatial pattern of compromised vehicles. A three-tier risk taxonomy converts disturbance metrics into actionable defense priorities for practitioners. Across all simulation scenarios, EV platoons exhibit lower velocity standard deviation, reduced spacing oscillations, and faster post-attack recovery compared to ICE counterparts, revealing an inherent stability advantage. These findings clarify how controller-to-powertrain coupling influences vulnerability and offer quantitative guidance for the detection and mitigation of attacks in mixed automated traffic.

Paper Structure

This paper contains 21 sections, 20 equations, 8 figures, 7 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. A Brief Background and Car-following Model
  4. Discretized Packet-Dropping Attack
  5. Phantom Attack
  6. Fixed-Speed Attack
  7. Blinding Attack
  8. Angular Velocity Attack
  9. Mixed Attack
  10. Summary
  11. Simulation and Experiment
  12. Experimental Setup
  13. Baseline Performance Analysis
  14. Discretized Packet-Dropping Attack Analysis
  15. Phantom Attack Analysis
  16. ...and 6 more sections

Figures (8)

  • Figure 1: Initial configuration of simulated Scenario III where three non-adjacent vehicles in red are subjected to targeted cyberattacks. The direction of traffic flow is counterclockwise.
  • Figure 2: Baseline spacing and velocity profiles for EVs and ICE vehicles in Scenario III under normal operating conditions (no cyberattacks).
  • Figure 3: Comparative analysis of spacing and velocity dynamics between EVs and ICE vehicles under DPDA in Scenario IV (6-second delay).
  • Figure 4: Comparative analysis of spacing and velocity dynamics between EVs and ICE vehicles under PA in Scenario IV.
  • Figure 5: Comparative analysis of spacing and velocity dynamics between EVs and ICE vehicles under FA in Scenario IV.
  • ...and 3 more figures