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On Evaluating Black-Box Explainable AI Methods for Enhancing Anomaly Detection in Autonomous Driving Systems.
Sazid Nazat1, Osvaldo Arreche1, Mustafa Abdallah2
1Electrical and Computer Engineering Department, Purdue School of Engineering and Technology, Indiana University-Purdue University Indianapolis, Indianapolis, IN 46202, USA.
Sensors (Basel, Switzerland)
|June 19, 2024
Summary
This study introduces a framework to evaluate explainable AI (XAI) techniques for detecting cybersecurity anomalies in autonomous vehicles (AVs). It assesses SHAP and LIME methods, offering insights for secure AV network development.
Area of Science:
- Cybersecurity
- Artificial Intelligence
- Autonomous Systems
Background:
- Autonomous vehicles (AVs) face cybersecurity risks targeting their networks.
- AI models are essential for detecting network anomalies in AVs.
- Explainable AI (XAI) is critical for understanding AI anomaly detection decisions.
Purpose of the Study:
- To introduce a comprehensive framework for assessing black-box XAI techniques in AV anomaly detection.
- To evaluate the effectiveness of global and local XAI methods in explaining AI behavior.
- To provide insights into the strengths and limitations of XAI for AV cybersecurity.
Main Methods:
- Developed a framework evaluating XAI techniques using six metrics: descriptive accuracy, sparsity, stability, efficiency, robustness, and completeness.
- Assessed two black-box XAI techniques: SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations).
- Applied XAI techniques to identify key features for anomaly classification on VeReMi and Sensor datasets.
Main Results:
- Evaluated SHAP and LIME against the six metrics using two AV datasets.
- Identified primary features crucial for classifying anomalous AV behavior using XAI.
- Provided a comparative analysis of SHAP and LIME performance in the context of AV anomaly detection.
Conclusions:
- The study advances the deployment of black-box XAI for real-world anomaly detection in autonomous driving.
- Offers valuable insights into the capabilities and constraints of current XAI methods in critical AV domains.
- Facilitates more robust and transparent cybersecurity solutions for autonomous vehicle networks.
