Modern automotive architectures are complex and often comprise of hundreds of electronic control units (ECUs). These ECUs provide diverse services including infotainment, telematics, diagnostics, advanced driving assistance, and many others. The availability of such services is mainly attained by the increasing connectivity with the external world, thus expanding the attack surface. In recent years, automotive original equipment manufacturers (OEMs) and ECU suppliers have become cautious of cyber attacks and have begun fortifying the most vulnerable systems, with hardware-based security modules that enable sandboxing, secure boot, secure software updates and end-to-end message authentication. Nevertheless, insecure legacy ECUs are still in-use in modern vehicles due to price and design complexity issues. Legacy ECUs depend on simple microcontrollers, that lack any kind of hardware-based security. This makes it essential to bridge the gap between modern and legacy ECUs through software-based security by which cyber attacks can be mitigated, thus enhancing the security of vehicles. This paper provides one more step towards highly secure vehicles by introducing a lightweight software-based security framework which provides legacy ECUs with software-based virtualization and protection features along with custom security services. We discuss the motivation for pure software-based approaches, explore the various requirements and advantages obtained, and give an initial insight of the design rationale. Furthermore, we provide a proof of concept implementation and evaluation with a demonstrative use case illustrating the importance of such framework in delivering new diagnostics security services to legacy ECUs.
Accurate indoor positioning remains an open research question. Existing solutions are either expensive, shortrange, or inaccurate. A new approach is therefore required to cost-effectively and accurately support localization in large indoor environments. We tackle this problem by introducing a practical optical localization scheme, called OSLo, that costeffectively scales to support large buildings. OSLo uses a meshed network of low-cost cameras as localization anchors and smart LEDs as tags, which transmit their IDs and context sensor data over the meshed cameras using optical communication. OSLo is capable of localizing dense deployments of tags with an accuracy of under 1 meter at a distance of 35 meters from the localization anchor. Furthermore, smart LED tags can be manufactured for less than $1. We systematically evaluate the performance of OSLo in the context of a real-world car localization use-case at Ford Motor Company in Germany and demonstrate promising results in terms of detection distance, and localization accuracy.
Modern vehicles are governed by a network of Electronic Control Units (ECUs), which are programmed to sense inputs from the driver and the environment, to process these inputs, and to control actuators that, e.g., regulate the engine or even control the steering system. ECUs within a vehicle communicate via automotive bus systems such as the Controller Area Network (CAN), and beyond the vehicles boundaries through upcoming vehicle-to-vehicle and vehicle-to-infrastructure channels. Approaches to manipulate the communication between ECUs for the purpose of security testing and reverse-engineering of vehicular functions have been presented in the past, all of which struggle with automating the detection of system change in response to message injection. In this paper we present our findings with fuzzing CAN networks, in particular while observing individual ECUs with a sensor harness. The harness detects physical responses, which we then use in a oracle functions to inform the fuzzing process. We systematically define fuzzers, fuzzing configurations and oracle functions for testing ECUs. We evaluate our approach based on case studies of commercial instrument clusters and with an experimental framework for CAN authentication. Our results show that the approach is capable of identifying interesting ECU states with a high level of automation. Our approach is applicable in distributed cyberphysical systems beyond automotive computing.
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