ACHILLES: A Machine Learning Framework for Explainable and Generalized Automotive Intrusion Detection System
This paper addresses the need for an explainable and generalized intrusion detection system (IDS) for the invehicle networks (IVNs). While machine learning (ML)-based IDS solutions show promising performance, there are still some challenges, such as t
Authors
- Mikael Gidlund (ORCID: https://orcid.org/0000-0003-0873-7827)
- Aamir Mahmood (ORCID: https://orcid.org/0000-0003-3717-7793)
- Joakim Rosell
- Antonios Lalas (ORCID: https://orcid.org/0000-0002-5337-161X)
- Sarder Fakhrul Abedin (ORCID: https://orcid.org/0000-0002-2966-6469)
- Mahshid Helali Moghadam (ORCID: https://orcid.org/0000-0003-3354-1463)
- Zhu Han
- Kyi Thar
- Nishat I Mowla (ORCID: https://orcid.org/0000-0002-3687-6755)
- Konstantinos Giapantzis
- Kabir Fahria
Institutions
- Scania (Sweden) (SE)
- RISE Research Institutes of Sweden (SE)
- Information Technologies Institute (GR)
- University of Houston (US)
- Mid Sweden University (SE)
Publication Details
- Published
- 2025-08-25
- DOI
- https://doi.org/10.36227/techrxiv.175616128.87412093/v1
- Primary Topic
- Anomaly Detection Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00