Our team is the core member of the national Hungarian working group, which is responsible for the establishment of the Automotive Cybersecurity Test and Certification Center at ZalaZONE. Due to this membership, our Department as direct experiences from the state-of-the-art scientific methods and concepts in the field of Automotive Cybersecurity.
We have traditionally strong cooperation with the actors of the Hungarian automotive ecosystem (especially considering the most important TIER1 market actors), which makes it possible to receive information about the most relevant industry-related issues from the automotive cybersecurity sector.
The Safety and Security Research Team pays particular attention to the interaction and interrelationship of automotive safety and security co-engineering.
Accordingly, we focus our research resources to develop integrated design and analysis methodologies that can consider the interaction of automotive safety and security of road domains together, especially considering the CAV related cybersecurity issues, such as IDS solutions, threat analysis, scenario-based testing, and specific domains of PKI management.
Beyond this, our team has outstanding competencies in the field of automotive forensics investigations. In this field, our primary research orientation related to CAVs focuses on the evaluation possibilities of accidents related to highly automated vehicles (including accident reconstruction methods, the investigation of the most relevant conflict types, and liability issues).
Development of safety testing framework for V2X-based application, especially considering the relevant cybersecurity sensitive network performance metrics
Inter-vehicular communication network testing methods
Pauer, G., & Török, Á. (2022). Introducing a novel safety assessment method through the example of a reduced complexity binary integer autonomous transport model. Reliability Engineering & System Safety, 217, 108062.
Pauer, G., & Török, Á. (2021). Binary integer modeling of the traffic flow optimization problem, in the case of an autonomous transportation system. Operations Research Letters, 49(1), 136-143.
Pethő, Z., Török, Á., & Szalay, Z. (2021). A survey of new orientations in the field of vehicular cybersecurity, applying artificial intelligence based methods. Transactions on Emerging Telecommunications Technologies, 32(10), e4325.
Ghadi, M., Sali, Á., Szalay, Z., & Török, Á. (2021). A new methodology for analyzing vehicle network topologies for critical hacking. Journal of Ambient Intelligence and Humanized Computing, 12(7), 7923-7934.
Ghadi, M., & Török, Á. (2019). A comparative analysis of black spot identification methods and road accident segmentation methods. Accident Analysis & Prevention, 128, 1-7.
Lengyel, H., Remeli, V., & Szalay, Z. (2021). A collection of easily deployable adversarial traffic sign stickers. at-Automatisierungstechnik, 69(6), 511-523.
Lengyel, H., Tettamanti, T., & Szalay, Z. (2020). Conflicts of automated driving with conventional traffic infrastructure. IEEE Access, 8, 163280-163297.
Kazár, TM, Pethő, Z., Vida, G., & Török, Á. (2022). Simulation of Road Traffic Accidents Related to ADAS Systems in PreScan. In The First Conference on ZalaZONE Related R&I Activities of Budapest University of Technology and Economics 2022 (pp. 39-43).