EASI: Edge-Based Sender Identification on Resource-Constrained Platforms for Automotive Networks

In vehicles, internal Electronic Control Units (ECUs) are increasingly prone to adversarial exploitation over wireless connections due to ongoing digitalization.Controlling an ECU allows an adversary to send messages to the internal vehicle bus and thereby to control various vehicle functions.Access to the Controller Area Network (CAN), the most widely used bus technology, is especially severe as it controls brakes and steering.However, state of the art receivers are not able to identify the sender of a frame.Retrofitting frame authenticity, e.g. through Message Authentication Codes (MACs), is only possible to a limited extent due to reduced bandwidth, low payload and limited computational resources.To address this problem, observation in analog differences of the CAN signal was proposed to determine the actual sender.Some of the prior approaches exhibit good identification and detection rates, however require high sampling rates and a high computing effort.With EASI we significantly reduce the required resources and at the same time show increased identification rates of 99.98% by having no false positives in a prototype structure and two series production vehicles.In comparison to the most lightweight approach so far, we have reduced the memory footprint and the computational requirements by a factor of 168 and 142, respectively.In addition, we show the feasibility of EASI and thus demonstrate for the first time that voltage-based sender identification is realizable using comprehensive signal characteristics on resource-constrained platforms.Due to the lightweight design, we achieved a classification in under 100 µs with a training time of 2.61 seconds.We also showed the ability to adapt the system to incremental signal changes during operation.Since cost effectiveness is of utmost importance in the automotive industry due to high production volumes, the achieved improvements are significant and necessary to realize sender identification.