Misbehavior Detection Using Collective Perception under Privacy Considerations

TL;DR

This paper enhances misbehavior detection in cooperative ITS by leveraging collective perception data, improving accuracy while preserving privacy through pseudonym IDs.

Contribution

It introduces a method that uses collective perception messages to improve misbehavior detection performance under privacy constraints.

Findings

Reduced false positive rate by approximately 15 percentage points.

Maintained high true positive detection rate.

Validated effectiveness using realistic traffic scenarios.

Abstract

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).