@inproceedings{WeissRennerMottoketal., author = {Weiss, Nils and Renner, Sebastian and Mottok, J{\"u}rgen and Matoušek, V{\´a}clav}, title = {Automated Threat Evaluation of Automotive Diagnostic Protocols}, series = {ESCAR USA, 2021, Virtual}, booktitle = {ESCAR USA, 2021, Virtual}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-29882}, pages = {20}, abstract = {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.}, language = {en} } @misc{ReuterLangerHauseretal., author = {Reuter, Rebecca and Langer, Thomas and Hauser, Florian and Muckelbauer, Daniel and Gegenfurtner, Andreas}, title = {Automatic generation of areas of interest in eye tracking: The case of software engineering}, series = {Earli Book of Abstracts}, journal = {Earli Book of Abstracts}, publisher = {RWTH Aachen}, language = {en} }