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Diagnostic protocols in automotive systems can offer a huge attack surface with devastating impacts if vulnerabilities are present. This paper shows the application of active automata learning techniques for reverse engineering system state machines of automotive systems. The developed black-box testing strategy is based on diagnostic protocol communication. Through this approach, it is possible to automatically investigate a highly increased attack surface. Based on a new metric, introduced in this paper, we are able to rate the possible attack surface of an entire vehicle or a single Electronic Control Unit (ECU). A novel attack surface metric allows comparisons of different ECUs from different Original Equipment Manufacturers (OEMs), even between different diagnostic protocols. Additionally, we demonstrate the analysis capabilities of our graph-based model to evaluate an ECUs possible attack surface over a lifetime.
In the beginning of every security analysis or penetration test of a system, information about the target has to be gathered. On IT-Systems a port scan is usually performed as a first step of an investigation. Since the communication protocols differ in automotive systems, generic port scanning tools can’t be used for a security analysis of CANs.
More complex protocols have a higher likelihood of implementation errors and bugs. On CAN networks, such payloads are transferred through International Standard Transport Protocol (ISO-TP) communication. We designed a new methodology to identify ISO-TP endpoints in automotive networks. Every of these endpoints can provide exploitable application layer protocols and therefor has to be considered during penetration testing and security analysis.
We contribute a new scan approach for the automated evaluation of possible attack surfaces in automotive CAN networks which has a higher coverage and multiple advantages than state of the art approaches.
Car manufacturers define proprietary protocols to be used inside their vehicular networks, which are kept an industrial secret, therefore impeding independent researchers from extracting information from these networks. This article describes a statistical and a neural network approach that allows reverse engineering proprietary controller area network (CAN)-protocols assuming they were designed using the data base CAN (DBC) file format. The proposed algorithms are tested with CAN traces taken from a real car. We show that our approaches can correctly reverse engineer CAN messages in an automated manner.
Die Beherrschung von Komplexität ist eine der größten Engineering-Herausforderungen des 21. Jahrhunderts. Themen wie das „Internet der Dinge“ (IoT) und „Industrie 4.0“ beschleunigen diesen Trend. Die modellgetriebene Entwicklung leistet einen entscheidenden Beitrag, um diesen Herausforderungen erfolgreich begegnen zu können.
Die Autoren geben einen fundierten Einstieg und praxisorientierten Überblick über die Modellierung von Software für eingebettete Systeme von den Anforderungen über die Architektur bis zum Design, der Codegenerierung und dem Testen. Für jede Phase werden Paradigmen,
Methoden, Techniken und Werkzeuge beschrieben und ihre praktische Anwendung in den Vordergrund gestellt. Darüber hinaus wird auf die Integration von Werkzeugen, funktionale Sicherheit und Metamodellierung eingegangen sowie die Einführung eines modellbasierten Ansatzes in einer Organisation und die Notwendigkeit zum lebenslangen Lernen erläutert.
Der Leser erfährt in diesem Buch, wie ein modellbasiertes Vorgehen nutzbringend in der Praxis für die Softwareentwicklung eingesetzt wird. Das Vorgehen wird unabhängig von Modellierungswerkzeugen vorgestellt. Zahlreiche Beispiele – exemplarisch auch auf Basis konkreter Werkzeuge – helfen bei der praktischen Umsetzung.
New computing-intensive applications such as assisted or highly automated driving are rapidly expanding the domain of safety-critical embedded systems, driven by the vision of the driverless car. This development makes it necessary to use high performance multi-core systems which are commercially available and provide more parallelism in terms of redundant execution units, however, at the cost of being less reliable. With the continuous down-scaling of semiconductor technology, computing hardware exhibits an increasing vulnerability against random hardware faults. Since these high-performance controller provide less or no hardware redundancy to ensure a safe execution of the application, software-only fault tolerance approaches are under current investigation. Our Scalable Software Support for Dependable Embedded Systems (S3DES) approach achieves fault tolerance by utilizing software-based triple modular redundancy for computational and optimized arithmetic encoded voter processes to ensure fault detection and error handling on application level. In S3DES voters are replicated to allow the compensation of voting failures. However, new challenges with regard to error propagation and multiple voting result outputs are introduced by this extension. We describe how mutual voter monitoring and threshold value checks could be used to establish a hierarchy among the replicated voters without re-introducing a reliability bottleneck in the sense of a single point of failure and resolve the aforementioned challenges.
Forschungsbericht 2017
(2017)
Modelling approaches have to satisfy certain criteria in order to sufficiently encompass the characteristics of dependable heterogenous multi- and many-core system architectures. This work-in-progress paper gives an overview of modern modelling approaches and their related research projects, particularly those regarding domain specific architecture description languages, as well as of the specific challenges of dependable systems and heterogenous multi- and many-core designs, i.e. scheduling techniques for real-time requirements and concerns regarding functional safety. Furthermore, an ongoing research effort in order to identify a set of criteria for evaluating the eligibility of modelling approaches for the task of adequately representing these systems and their specific characteristics is presented.
In this paper we describe a proposed framework for Cooperative Adaptive Personalized Education for Life-long Learning and Activation (CAPELLA). CAPELLA comprises four models: A Knowledge Organization Model, a Learning Process Model, a Community Collaboration Model and a Gaming Model. The underlying pedagogical approach is based on the four pillars of learning: Learning to know, learning to do, learning to collaborate and learning to be. Based on these models and principles, CAPELLA provides a theoretical framework that may assist IT developers to develop state-of-the-art educational platforms and advanced learning applications. Using an example of a UML Diagramming Workshop we demonstrate how CAPELLA incorporates the principles of Adaptive Learning.
Neu eingeführte Funktionen in der Automobilindustrie, wie zum Beispiel das autonome Fahren, erfordern den Einsatz von performanten Mehrkernprozessoren sowie von komplexen (POSIXkompatiblen) Betriebssystemen. Im Rahmen des branchenspezifischen Preisdrucks kommt es zudem zu einer Konsolidierung von Steuergeräten. Gleichzeitig erfordert der Einsatz im Automobil hohe funktionale Sicherheit (ASIL-Level), was unter anderem robuste Echtzeiteigenschaften der verwendeten Hard- und Software voraussetzt. Als Folge dessen werden zur Trennung von harten und weichen Echtzeitsystemen auf derselben Hardware Hypervisoren eingesetzt. Dieses Paper beleuchtet die Latenzauswirkungen diverser Softwarekonfigurationen auf Hardware der nächsten Generation mithilfe eines vorgestellten Testsetups und dessen Ergebnissen.