TY - CHAP A1 - Weiss, Nils A1 - Renner, Sebastian A1 - Mottok, Jürgen A1 - Matoušek, Václav T1 - Automated Threat Evaluation of Automotive Diagnostic Protocols T2 - ESCAR USA, 2021, Virtual N2 - 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. KW - Automotive Diagnostic Protocols KW - Security Metrics KW - Automated Network Scan Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-29882 ER - TY - JOUR A1 - Weiss, Nils A1 - Pozzobon, Enrico A1 - Mottok, Jürgen A1 - Matoušek, Václav T1 - Automated Reverse Engineering of CAN Protocols JF - Neural Network World N2 - 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. Y1 - 2021 U6 - https://doi.org/10.14311/NNW.2021.31.015 SN - 1210-0552 VL - 31 IS - 4 SP - 279 EP - 295 ER - TY - CHAP A1 - Jahn, Sabrina A1 - Weiss, Nils A1 - Akcakoca, Ugur A1 - Mottok, Jürgen ED - Gómez Chova, Luis ED - López Martínez, A. ED - Candel Torres, I. T1 - Under The Hood - A Concept for Virtualized Automotive Security Education T2 - EDULEARN21: Proceedings, 13th International Conference on Education and New Learning Technologies, Online Conference. 5-6 July, 2021 N2 - In recent years, our society faced a massive interconnection of computer-based everyday objects, which opens these items for cyber-attacks. Dependent on the physical capabilities, successful attacks can vary from data exposure or a loss of functionality to a threat to life and limb. Connected- and autonomous vehicles are extremely safety-critical systems with a huge damage potential. This global trend, together with existing and upcoming regulations (ISO 270xx, ISO 21434, UNECE WP.29, UNECE R155), and the lack of qualified professionals create a tension field for the entire automotive industry. Hence, new education concepts for engineers of safety-critical and connected systems are necessary to secure our daily and future systems against cyber-attacks and raise awareness and knowledge of the topic of IT-Security. Existing automotive security education systems have one common problem: All systems are hardware-based and therefore have very steep learning curves for beginners. Hardware-based systems, in general, are expensive in their initial costs, require regular maintenance, and add diverse operational difficulties independent of the aspired education goal. Additionally, the global pandemic increased the necessity of virtual education concepts for security training in cyber-physical systems. Therefore, we present a novel concept for the education of cyber-security professionals for automotive systems based on discovery and problem-based learning in a virtual learning environment (VLE). Our concept contains individual exercises focusing on the topics of vulnerabilities and attacks in automotive networks and systems. Each exercise relates those topics to the corresponding security goals and countermeasures for mitigation. The learners work collaboratively in a self-contained manner within the VLE to acquire the necessary information to answer questions or find a solution to the given problem. To consider the heterogeneous background (e.g. knowledge, experience, preconceptions) of the learners, the topics can be presented in different difficulties, enabling an adaptable learning environment and different learning trajectories within the exercises. The concept is based on a VLE, consisting of automotive networks and components, which simulate the behavior of a vehicle. This environment provides a hands-on, "real-life" scenario, which allows discovery and problem-based learning in a realistic, but cheap and scalable education environment. Furthermore, virtualization removes common difficulties, always present in training on real hardware. This aims to decrease complexity, prevent learning obstacles related to hardware handling, and enables a location-independent learning environment. The target group of our education concept is Bachelor and Master students of computer science, engineering (e.g. electrical engineering, mechatronics), or similar studies, and (experienced) engineers from the industry. In summary, our publication contains two contributions. We present an adaptable virtual learning environment for automotive security education, combined with an educational concept based on discovery and problem-based learning techniques. The goals of our concept are the education of cyber-security professionals for safety-critical, connected automotive systems and the support of life-long learning reaching from academic education to training in the industry. KW - automotive systems KW - discovery learning KW - higher education KW - inductive teaching KW - problem-based learning KW - security education KW - virtual learning environment Y1 - 2021 SN - 978-84-09-31267-2 U6 - https://doi.org/10.21125/edulearn.2021.1231 SN - 2340-1117 SP - 6109 EP - 6118 ER -