Our new paper is available now here.
You can cite it like this: B. Koltai, G. Ács and A. Gazdag, CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation, Euro S&P – ACSW26, 2026.
Modern vehicles are no longer just mechanical machines. They are complex computer systems on wheels, with many electronic control units constantly talking to each other. One of the main communication systems inside vehicles is the Controller Area Network, or CAN bus.
The CAN bus was designed to be fast and reliable, but not necessarily secure. As cars have become more connected, this has become a serious concern. If an attacker gains access to the vehicle network, they may try to inject fake messages, block legitimate ones, or modify vehicle signals. This is why many researchers have worked on intrusion detection systems, or IDSs, for automotive networks.
These systems are meant to detect suspicious activity on the CAN bus. But there is an important question behind this work:
If an IDS performs well in one study, can we trust that it will also work well in another vehicle, another dataset, or another attack scenario? Or in other words: Are some current methods too dependent on the specific datasets they used?
Continue reading CAN We Trust Your Results? A Cross-Dataset Study of Automotive IDS Evaluation
