Internal Network Monitoring with DNN and Network Tomography for In-Vehicle Networks

With the advancements in Internet-of-Things (IoTs), particularly in Internet-of-Vehicles (IoVs), the vehicle becomes more vulnerable to more attack types caused by connecting the vehicle to the outside world. Moreover, the shift towards automotive Ethernet exposes the vehicle to IP-based attacks similar to attacks on computer networks. Most of such attacks tamper with the internal network components in order to gain control or disable some (or all) of the vehicle's functions. To this end, in this work, we study two in-vehicle network monitoring approaches based on network tomography. The first approach relies purely on deep neural network (DNN) and we call it DNN-based tomography approach, while the second was proposed in a previous work and it uses algebraic network tomography with deep neural network, we call this one DNN-based algebraic approach. We evaluated the inference performance of both approaches using simulations and found that the DNN-based algebraic tomography approach outperforms DNN-based tomography approach with less inference error of about 4.5μs.