  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.
