  Connected cars are susceptible to cyberattacks. Security and safety of future
vehicles highly depend on a holistic protection of automotive components, of
which the time-sensitive backbone network takes a significant role. These
onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as
versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for
security. Still a thorough evaluation of anomaly detection methods in the
context of hard real-time operations, automotive protocol stacks, and domain
specific attack vectors is missing along with appropriate input datasets. In
this paper, we present an assessment framework that allows for reproducible,
comparable, and rapid evaluation of detection algorithms. It is based on a
simulation toolchain, which contributes configurable topologies, traffic
streams, anomalies, attacks, and detectors. We demonstrate the assessment of
NADSs in a comprehensive in-vehicular network with its communication flows, on
which we model traffic anomalies. We evaluate exemplary detection mechanisms
and reveal how the detection performance is influenced by different
combinations of TSN traffic flows and anomaly types. Our approach translates to
other real-time Ethernet domains, such as industrial facilities, airplanes, and
UAVs.
