Connected vehicles are becoming commonplace. A constant connection between vehicles and a central server enables new features and services. This added connectivity raises the likelihood of exposure to attackers and risks unauthorized access. A possible countermeasure to this issue are intrusion detection systems (IDS), which aim at detecting these intrusions during or after their occurrence. The problem with IDS is the large variety of possible approaches with no sensible option for comparing them. Our contribution to this problem comprises the conceptualization and implementation of a testbed for an automotive real-world scenario. That amounts to a server-side IDS detecting intrusions into vehicles remotely. To verify the validity of our approach, we evaluate the testbed from multiple perspectives, including its fitness for purpose and the quality of the data it generates. Our evaluation shows that the testbed makes the effective assessment of various IDS possible. It solves multiple problems of existing approaches, including class imbalance. Additionally, it enables reproducibility and generating data of varying detection difficulties. This allows for comprehensive evaluation of real-time, remote IDS.