Building Trustworthy ML Systems for Automotive Cybersecurity

Building Trustworthy ML Systems for Automotive Cybersecurity

The automotive industry is rapidly evolving with increasing connectivity and autonomy. As vehicles become more connected, they also become more vulnerable to cyberattacks. Traditional security measures are no longer sufficient, and machine learning (ML) has emerged as a powerful tool for detecting anomalies in automotive networks.

The Challenge

Modern vehicles rely on Controller Area Network (CAN) buses for internal communication. These networks were designed decades ago with minimal security considerations, making them attractive targets for attackers. Detecting malicious activity on CAN buses requires sophisticated anomaly detection techniques.

Machine Learning Approach

In my research, I evaluated several ML models for CAN bus anomaly detection:

• **Random Forest**: Excellent baseline performance with good interpretability
• **XGBoost**: Superior accuracy for complex patterns
• **Isolation Forest**: Effective for detecting rare anomalies

Each model has its strengths, but accuracy alone isn't enough for safety-critical systems.

The Trust Problem

One of the biggest challenges in deploying ML models for automotive security is trust. Engineers, regulators, and end-users need to understand why a model flagged a particular message as anomalous. Black-box models, no matter how accurate, face adoption barriers in safety-critical domains.

Explainable AI Solutions

To address this, I integrated explainability techniques:

SHAP (SHapley Additive exPlanations)

SHAP provides consistent, theoretically grounded explanations for model predictions. It shows how each feature contributes to the final decision, making it easier to understand and debug the model.

LIME (Local Interpretable Model-agnostic Explanations)

LIME creates local approximations of the model's behavior, explaining individual predictions in human-understandable terms.

Implementation Architecture

The system uses a microservice-based architecture:

• **Flask Backend**: RESTful API for model inference
• **Docker Containers**: Isolated, scalable services
• **React Frontend**: Real-time visualization of detection results

This architecture ensures scalability, maintainability, and the ability to update models without disrupting the entire system.

Key Takeaways

1. **Accuracy isn't everything**: Trust and interpretability are crucial for safety-critical systems 2. **Explainability enhances adoption**: Clear explanations help engineers understand and trust ML decisions 3. **Architecture matters**: Microservices enable flexible, maintainable ML systems 4. **Real-world deployment**: Production systems need more than just good models—they need robust infrastructure

Future Directions

The field of trustworthy ML for automotive cybersecurity is still evolving. Future work could explore:

• Federated learning for collaborative threat detection
• Real-time explainability for edge devices
• Integration with vehicle-to-vehicle communication systems

Building trustworthy ML systems requires balancing accuracy, interpretability, and practical deployment considerations. By combining strong models with explainability techniques, we can create systems that are both effective and trusted.