# VeReMi: A Dataset for Comparable Evaluation of Misbehavior Detection in VANETs

arXiv: https://arxiv.org/abs/1804.06701 · HTML: https://mlsanity.com/paper/1804.06701/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 14 of 14)

- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Machine Learning for Security in Vehicular Networks: A Comprehensive Survey](https://mlsanity.com/paper/2105.15035/) (2021)
- [MistralBSM: Leveraging Mistral-7B for Vehicular Networks Misbehavior Detection](https://mlsanity.com/paper/2407.18462/) (2024)
- [AttentionGuard: Transformer-based Misbehavior Detection for Secure Vehicular Platoons](https://mlsanity.com/paper/2505.10273/) (2025)
- [Federated Learning for Misbehaviour Detection with Variational Autoencoders and Gaussian Mixture Models](https://mlsanity.com/paper/2405.09903/) (2024)
- [An Intelligent Native Network Slicing Security Architecture Empowered by Federated Learning](https://mlsanity.com/paper/2410.05312/) (2024)
- [Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection](https://mlsanity.com/paper/2512.15503/) (2025)
- [Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset](https://mlsanity.com/paper/2202.07704/) (2022)
- [XAI-based Feature Ensemble for Enhanced Anomaly Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2410.15405/) (2024)
- [FedSecureFormer: A Fast, Federated and Secure Transformer Framework for Lightweight Intrusion Detection in Connected and Autonomous Vehicles](https://mlsanity.com/paper/2512.24345/) (2025)
- [FAST-IDS: A Fast Two-Stage Intrusion Detection System with Hybrid Compression for Real-Time Threat Detection in Connected and Autonomous Vehicles](https://mlsanity.com/paper/2512.24391/) (2025)
- [PAMPOS: Causal Transformer-based Trajectory Prediction for Attack-Agnostic Misbehavior Detection in V2X Networks](https://mlsanity.com/paper/2605.06833/) (2026)
- [SPADE: SPaT Attack Detection from the Connected Vehicle's Perspective](https://mlsanity.com/paper/2609.02741/) (2026)

## Abstract (arXiv)

Vehicular networks are networks of communicating vehicles, a major enabling technology for future cooperative and autonomous driving technologies. The most important messages in these networks are broadcast-authenticated periodic one-hop beacons, used for safety and traffic efficiency applications such as collision avoidance and traffic jam detection. However, broadcast authenticity is not sufficient to guarantee message correctness. The goal of misbehavior detection is to analyze application data and knowledge about physical processes in these cyber-physical systems to detect incorrect messages, enabling local revocation of vehicles transmitting malicious messages. Comparative studies between detection mechanisms are rare due to the lack of a reference dataset. We take the first steps to address this challenge by introducing the Vehicular Reference Misbehavior Dataset (VeReMi) and a discussion of valid metrics for such an assessment. VeReMi is the first public extensible dataset, allowing anyone to reproduce the generation process, as well as contribute attacks and use the data to compare new detection mechanisms against existing ones. The result of our analysis shows that the acceptance range threshold and the simple speed check are complementary mechanisms that detect different attacks. This supports the intuitive notion that fusion can lead to better results with data, and we suggest that future work should focus on effective fusion with VeReMi as an evaluation baseline.