Neural network based partial tomography for in-vehicle network monitoring

In-vehicle network monitoring is one of the important elements in vehicular network management and security. Most of the existing network monitoring approaches rely on measuring every part of the network. Such approaches overburden the network by transmitting active probes. In this work, we propose a new in-vehicle network monitoring approach that benefits from network tomography and the advances in deep learning to infer the network delay performance. Specifically, the available measurements can be used to estimate the performance of the remaining network where direct measurements cannot be applied. Performance evaluation has been conducted using in-vehicle network simulation with different TSN (Time-Sensitive Network) traffics and the proposed monitoring approach shows the delay estimation accuracy of up to 99%.