TY - JOUR A1 - Fuxen, Philipp A1 - Schönhärl, Stefan A1 - Schmidt, Jonas A1 - Gerstner, Mathias A1 - Jahn, Sabrina A1 - Graf, Julian A1 - Hackenberg, Rudolf A1 - Mottok, Jürgen T1 - A Cybersecurity Education Platform for Automotive Penetration Testing JF - International Journal On Advances in Security N2 - The paper presents a penetration testing framework for automotive IT security education and evaluates its realization. The automotive sector is changing due to automated driving functions, connected vehicles, and electric vehicles. This development also creates new and more critical vulnerabilities. This paper addresses a possible countermeasure, automotive IT security education. Some existing solutions are evaluated and compared with the created Automotive Penetration Testing Education Platform (APTEP) framework. In addition, the APTEP architecture is described. It consists of three layers representing different attack points of a vehicle. The realization of the APTEP is a hardware case and a virtual platform referred to as the Automotive Network Security Case (ANSKo). The hardware case contains emulated control units and different communication protocols. The virtual platform uses Docker containers to provide a similar experience over the internet. Both offer two kinds of challenges. The first introduces users to a specific interface, while the second combines multiple interfaces, to a complex and realistic challenge. This concept is based on modern didactic theories, such as constructivism and problem-based/challenge-based learning. Computer Science students from the Ostbayerische Technische Hochschule (OTH) Regensburg experienced the challenges as part of a elective subject. In an online survey evaluated in this paper, they gave positive feedback. Also, a part of the evaluation is the mapping of the ANSKo and the maturity levels in the Software Assurance Maturity Model (SAMM) practice Education & Guidance as well as the SAMM practice Security Testing. The scientific contribution of this paper is to present an APTEP, a corresponding learning concept and an evaluation method. KW - Challenge-based Learning KW - Education Framework KW - Penetration Testing KW - Automotive KW - IT-Security Education Y1 - 2022 UR - http://www.iariajournals.org/security/sec_v15_n34_2022_paged.pdf SN - 1942-2636 VL - 15 IS - 3&4 SP - 106 EP - 118 PB - IARIA ER - TY - CHAP A1 - Reichel, Tobias A1 - Gerstner, Mathias A1 - Schiller, Leo A1 - Attenberger, Andreas A1 - Hackenberg, Rudolf A1 - Dološ, Klara T1 - A forensic analysis of GNSS spoofing attacks on autonomous vehicles T2 - Cloud Computing 2025 : The Sixteenth International Conference on Cloud Computing, GRIDs, and Virtualization, 06.-10. April 2025, Valencia N2 - Global Navigation Satellite Systems (GNSSs) are essential for modern technology, enabling precise geographic positioning in aviation, maritime shipping, and automotive systems. In the future, their role will be even more critical for autonomous vehicles, which rely on accurate localization for navigation and decision-making. However, the increasing connectivity of autonomous vehicles exposes them to cyber threats, including GNSS spoofing attacks, which manipulate location data to mislead onboard systems. As reliance on GNSS grows, so does the risk posed by spoofing attacks, making it a critical security concern. This paper describes GNSS spoofing attacks on autonomous vehicles, focusing on their detection both during and after an attack. Furthermore, we analyze data storage strategies to facilitate effective forensic analysis. We highlight the importance of position, signal, and camera data, which should be preserved to ensure a comprehensive forensic investigation. Finally, we suggest a simulation setup that enables studying which data could be used for a forensic investigation. Additionally, we examine established data frameworks and decide whether they are suitable for detecting GNSS spoofing attacks. Y1 - 2025 UR - https://www.thinkmind.org/articles/cloud_computing_2025_1_50_28008.pdf SN - 978-1-68558-258-6 SN - 2308-4294 SP - 32 EP - 39 ER -