Simulation of structural effects in embedded systems and visualization of dependencies according to an intended attack or manipulation

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Abstract

The purpose of this workshop contribution is the interaction between networked embedded systems (such as controllers, sensors and actuators within a motor vehicle) and how these can be simulated. It aims to assess the potential impacts of IT security incidents in advance and plan protective measures without having to build a complete prototype. The feasibility of recognizing and visualising possible dependencies and structural effects in a complex networked system with embedded components (in this example: A motor vehicle with CAN bus become more and more relevant. One exemplary reason is that in contrast to earlier times, now the vehicle manufacturers are acting just as a systems integrator of supply components. This could result in a leakage of the total understanding and a lost track of the networked system elements. This development is intensified by the growing number of control units, sensors and actuators, which make the complete testing of all system components and their dependencies almost impossible. Derived from this the need for a simulation model that represents these possible interactions as extensively as possible is obvious. In a first research step selected control devices on a CAN network, and related automotive sensors and actuators are modelled by simulation in order to simulate freely and selectable event sequences, e.g. to carry out as part of penetration testing to monitor possible weaknesses in the system and resulting interactions. On the latter, particular attention is given, as the security area is in the main application focus of this simulation. © 2012 Springer-Verlag Berlin Heidelberg.

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APA

Kuhlmann, S., Fruth, J., Hoppe, T., & Dittmann, J. (2012). Simulation of structural effects in embedded systems and visualization of dependencies according to an intended attack or manipulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7613 LNCS, pp. 498–507). https://doi.org/10.1007/978-3-642-33675-1_47

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