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Detecting Switching Attacks On Traffic Flow Regulation For Changing Driving Patterns

Sanchita Ghosh, Tanushree Roy

TL;DR

This work proposes a cyberattack detection scheme to detect the compromised controller switching in ramp metering for an uncertain, multimodal macroscopic traffic operation of a freeway segment and proposes a bank of detectors corresponding to each admissible traffic mode that can compensate for the uncertain traffic mode of the freeway.

Abstract

Modern traffic management systems increasingly adopt hierarchical control strategies for improved efficiency and scalability, where a local traffic controller mode is chosen by a supervisory controller based on the changing large-scale driving patterns. Unfortunately, such local metering controllers are also vulnerable to cyberattacks that can disrupt the controller switching, leading to undesired, inefficient, and even unsafe traffic operations. Additionally, the detection of such attacks becomes challenging when the operational mode of the traffic is uncertain and the operational mode identification is delayed. Thus, in this work, we propose a cyberattack detection scheme to detect the compromised controller switching in ramp metering for an uncertain, multimodal macroscopic traffic operation of a freeway segment. In particular, we propose a bank of detectors corresponding to each admissible traffic mode that can compensate for the uncertain traffic mode of the freeway. Furthermore, we utilize backstepping tools along with Lyapunov function theory to achieve analytical performance guarantees for the detector, such as nominal exponential stability, anomaly/uncertainty-to-residual stability, robustness, and sensitivity. Finally, we demonstrate the efficacy of the proposed detection scheme through simulations of free traffic under realistic traffic parameters, uncertainties, and commonly occurring attack scenarios.

Detecting Switching Attacks On Traffic Flow Regulation For Changing Driving Patterns

TL;DR

This work proposes a cyberattack detection scheme to detect the compromised controller switching in ramp metering for an uncertain, multimodal macroscopic traffic operation of a freeway segment and proposes a bank of detectors corresponding to each admissible traffic mode that can compensate for the uncertain traffic mode of the freeway.

Abstract

Modern traffic management systems increasingly adopt hierarchical control strategies for improved efficiency and scalability, where a local traffic controller mode is chosen by a supervisory controller based on the changing large-scale driving patterns. Unfortunately, such local metering controllers are also vulnerable to cyberattacks that can disrupt the controller switching, leading to undesired, inefficient, and even unsafe traffic operations. Additionally, the detection of such attacks becomes challenging when the operational mode of the traffic is uncertain and the operational mode identification is delayed. Thus, in this work, we propose a cyberattack detection scheme to detect the compromised controller switching in ramp metering for an uncertain, multimodal macroscopic traffic operation of a freeway segment. In particular, we propose a bank of detectors corresponding to each admissible traffic mode that can compensate for the uncertain traffic mode of the freeway. Furthermore, we utilize backstepping tools along with Lyapunov function theory to achieve analytical performance guarantees for the detector, such as nominal exponential stability, anomaly/uncertainty-to-residual stability, robustness, and sensitivity. Finally, we demonstrate the efficacy of the proposed detection scheme through simulations of free traffic under realistic traffic parameters, uncertainties, and commonly occurring attack scenarios.

Paper Structure

This paper contains 15 sections, 1 theorem, 63 equations, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related works
  3. Research gap and contribution
  4. Problem Framework
  5. Macroscopic traffic model under cyberattack
  6. Cyberattack policies and their impact
  7. Cyberattack Detection Scheme
  8. Detector and error dynamics
  9. Analytic guarantees for detector performance
  10. Backstepping transformation of error dynamics
  11. Design of detector gains
  12. Simulation Results
  13. Case study I: DoS attack
  14. Case study II: FDI attack
  15. Conclusion

Key Result

Theorem 1

Consider a multi-modal freeway traffic modeled by flux-BC_nonlinearARZ and the corresponding bank of $m$ detectors omegaHat-y_Hat. The detection scheme is considered ES ES_criteria, AURS DSS_criteria, robust against uncertainties robust def r, and sensitive towards cyberattacks sen def r for every m where, Table tab:thm_var lists the parameters for upK4J-senLam, where $\mu_i > 0$, $i\in \{1,\cdot

Figures (6)

  • Figure 1: An overview picture for detecting switching cyberattacks on a ramp controller during multi-modal traffic operation.
  • Figure 2: Figure depicts the impact of controller switching on traffic behavior under nominal conditions and DoS attack. (a) Illustrates the safe headway extended from sunny to rainy weather under nominal switching, and (c) shows reduced traffic density, flux, and velocity under adjusted ramp metering that compensates for the adverse weather. Conversely, (b) shows the unsafe headway under a wrong controller in rainy weather, while (d) demonstrates the congestion build-up along with unsafe traffic flux and velocity due to unadjusted ramp metering under DoS attack, i.e., no controller switching.
  • Figure 3: Under DoS attack, the figure shows traffic density (top), flux (middle), and velocity (bottom).
  • Figure 4: Under DoS attack, the figure shows nominal and corrupted boundary ramp control (top) and attack detection with the generated residual (bottom).
  • Figure 5: Under FDI attack, the figure shows traffic density (top), flux (middle), and velocity (bottom).
  • ...and 1 more figures

Theorems & Definitions (2)

  • Theorem 1
  • proof