Physics-Based Attack Detection in Autonomous Vehicle Control Loops
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current security measures for autonomous vehicles' closed-loop control systems are inadequate in detecting and localizing attacks, particularly due to computational intensity, limited detection scope, and vulnerability to compromised ECUs, which can lead to safety and security risks.
Innovation Solution
A physics-based approach utilizing multiple state estimators to compute residual signals for attack detection and localization, leveraging physical models of system dynamics for real-time monitoring and fine-grained characterization of attacks, reducing false positives and computational load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple state estimators are used to compute residual signals for attack detection, then measurement precision and detection accuracy are improved, but device complexity and computational load increase
Solution Approach 1:
The system divides attack detection into multiple independent residual signal computations, each handled by a separate state estimator. Each estimator focuses on specific system states (vehicle speed, steering angle, acceleration), allowing parallel computation and modular architecture that reduces overall computational complexity while maintaining high detection accuracy.
Solution Approach 2:
Residual signals serve as intermediaries between the physical vehicle system and the attack detection logic. These residual signals capture deviations from expected physical behavior without requiring direct complex analysis of all system parameters, simplifying the detection process while preserving measurement precision.
2Speed
If physics-based models are used for real-time monitoring, then detection speed and response time are improved, but manufacturing complexity and system design difficulty increase
Solution Approach 1:
The physics-based models are self-contained and require minimal external calibration or adjustment. The state estimators use standard vehicle dynamics equations that inherently adapt to the specific vehicle configuration, reducing implementation complexity while enabling fast real-time detection.
Solution Approach 2:
The physics-based monitoring system serves multiple functions simultaneously: it provides attack detection, system state estimation, and anomaly characterization. This multi-functionality is achieved through a unified framework that leverages the same physical models for various detection purposes, simplifying implementation while maintaining high detection speed.
3Reliability
If fine-grained attack characterization is implemented, then reliability and security are improved, but loss of computational resources increases
Solution Approach 1:
The system applies different levels of analysis to different aspects of attack detection. Residual signals provide coarse-grained detection, while fine-grained characterization is only applied when anomalies are detected. This localized application of computational resources maintains high security while reducing overall energy consumption.
Solution Approach 2:
The system performs partial attack characterization by analyzing only the specific residual signals that indicate potential attacks. Rather than continuously analyzing all system parameters, the system focuses computational resources on relevant anomalies, achieving high reliability with reduced computational overhead.
Data Source
AI summary
Methods and apparatus relating to a physics-based approach for attack detection and/or localization in closed-loop controls for autonomous vehicles are described. In an embodiment, multiple state estimators are used to compute a set of residuals to detect, classify, and/or localize attacks. This allows for determination of an attacker's location and the kind of attack being perpetrated. Other embodiments are also disclosed and claimed.