# Misbehavior Detection Using Collective Perception under Privacy Considerations

arXiv: https://arxiv.org/abs/2111.03461 · HTML: https://mlsanity.com/paper/2111.03461/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2021.

## Built on by (most cited first, 3 of 3)

- [Vehicle-to-Everything Cooperative Perception for Autonomous Driving](https://mlsanity.com/paper/2310.03525/) (2023)
- [A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks](https://mlsanity.com/paper/2201.10500/) (2022)
- [Probabilistic Segmentation for Robust Field of View Estimation](https://mlsanity.com/paper/2503.07375/) (2025)

## Abstract (arXiv)

In cooperative ITS, security and privacy protection are essential. Cooperative Awareness Message (CAM) is a basic V2V message standard, and misbehavior detection is critical for protection against attacking CAMs from the inside system, in addition to node authentication by Public Key Infrastructure (PKI). On the contrary, pseudonym IDs, which have been introduced to protect privacy from tracking, make it challenging to perform misbehavior detection. In this study, we improve the performance of misbehavior detection using observation data of other vehicles. This is referred to as collective perception message (CPM), which is becoming the new standard in European countries. We have experimented using realistic traffic scenarios and succeeded in reducing the rate of rejecting valid CAMs (false positive) by approximately 15 percentage points while maintaining the rate of correctly detecting attacks (true positive).