V2X-SIEM: A Lightweight Security Information and Event Management Architecture for Connected and Autonomous Vehicles
The growing complexity of vehicular networks, from the implementation of Vehicle-to-Everything (V2X) communication technologies, poses a significant challenge to traditional Security Information and Event Management (SIEM) systems, which are often inflexible and unable to support timely threat detection in automotive contexts. This paper proposes a modular, lightweight, and open-source architecture for a SIEM system designed for Connected and Autonomous vehicles (CAVs). The proposed architecture is built on Elasticsearch and Kibana, and is configured for low latency, high scalability, and efficient event correlation from vehicular communication protocols. It includes components for event collection, analysis, and visualization, and processes vehicular security events nearly in real time while remaining resource efficient, which makes it suitable for deployment with edge computing. The contributions are demonstrated to have practical implications for vehicle security by bridging the strategies of enterprise SIEM capabilities and the unique demands of intelligent transportation systems.
Authors
- Manuel J. C. S. Reis (ORCID: https://orcid.org/0000-0002-8872-5721)
- Frederico Branco (ORCID: https://orcid.org/0000-0001-8434-4887)
- Carlos Serôdio (ORCID: https://orcid.org/0000-0003-4632-9664)
- António L.G. Valente (ORCID: https://orcid.org/0000-0002-5798-1298)
- Pedro Ferreira (ORCID: https://orcid.org/0000-0001-6955-8414)
- Rafael Abreu (ORCID: https://orcid.org/0009-0007-3415-2773)
Institutions
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- University of Trás-os-Montes and Alto Douro (PT)
- Association for the Development of Douro Viticulture (PT)
- INESC TEC (PT)
Publication Details
- Published
- 2025-08-07
- DOI
- https://doi.org/10.1109/acdsa65407.2025.11165947
- Citations
- 1
- Primary Topic
- Vehicular Ad Hoc Networks (VANETs)
- Type
- article
- Field-Weighted Citation Impact
- 0.73