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Explainable AI for Intrusion Detection Systems in Automotive

Reference number
Coordinator RISE Research Institutes of Sweden AB
Funding from Vinnova SEK 100 000
Project duration January 2025 - June 2025
Status Ongoing
Venture 6G - Competence supply
Call 6G - Supervision of degree work

Important results from the project

The project developed an explainable intrusion detection solution for Automotive Ethernet using Logical Neural Networks (LNN), demonstrating both high accuracy and interpretability. The project goals were fully achieved.

Expected long term effects

The solution can help make future massively connected vehicle networks more secure and transparent by promoting the use of explainable AI in safety-critical applications.

Approach and implementation

The project was carried out in four stages: data preparation, AI model training, development of explainability methods (SHAP, LNN), and performance evaluation. Collaboration between academia and industry worked well, and the project stayed on schedule.

The project description has been provided by the project members themselves and the text has not been looked at by our editors.

Last updated 22 July 2025

Reference number 2024-04248