@phdthesis{GomezBuquerin2024, author = {Gomez Buquerin, Kevin}, title = {Unveiling Hidden Knowledge: On the Effectiveness in Automotive Digital Forensics}, publisher = {Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg}, address = {Erlangen}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:573-44402}, pages = {xii, 123}, school = {Friedrich-Alexander-Universit{\"a}t Erlangen-N{\"u}rnberg}, year = {2024}, abstract = {The continuous introduction of new services in modern automotive systems increases their digital capabilities and growing significance for digital forensic investigations. Those investigations are necessary to reconstruct events of potential crimes, generate security learnings, or resolve insurance cases due to the increased attack surface resulting from the increasing digital capabilities of modern vehicles. Consequently, the acquisition of vehicle and vehicle ecosystem components, as well as analysis of the collected data, has become pivotal for investigators seeking to reconstruct events effectively. This dissertation proposes a novel approach to leverage a multi-layered model utilizing ontologies in automotive digital forensics investigations, facilitating more efficient and effective data analysis by combining information from diverse sources. By harnessing ontologies to reuse knowledge collected from past investigations, investigators can seamlessly combine data from multiple sources, unveil hidden knowledge, and establish connections that might have otherwise remained obscure, thereby aiding in the resolution of crimes and apprehending criminals. A multi-layered model is presented to showcase increased effectiveness achieved through knowledge reuse. The model comprises a query layer that serves as an interface between investigators and the knowledge layer, allowing automotive digital forensics stakeholders to pose forensic questions and hypotheses via SPARQL queries. These queries extract data from the knowledge layer, which consists of two ontologies developed for this study: the general vehicle ontology, offering comprehensive knowledge on modern vehicles, and the vehicle type ontology, containing vehicle-specific knowledge, such as information from a Tesla Autopilot. The knowledge is generated through the operational layer, which introduces digital forensics tools generating results in an ontology-ready format. Two real-world investigations were undertaken to validate the effectiveness of the proposed approach: a thorough examination of the Tesla Autopilot system and an in-depth inquiry involving a GM Airbag control module. Both investigations involved crucial evidence items highly relevant for event reconstruction. The results demonstrate a significant increase in the effectiveness of automotive digital forensics investigations using the multi-layered model. This improvement is validated through three evaluation criteria and twelve metrics to show the application proximity of the multi-layered model.}, language = {en} } @phdthesis{Thambi2018, author = {Thambi, Joel Luther}, title = {Reliability assessment of lead- free solder joint, based on high cycle fatigue \& creep studies on bulk specimen}, publisher = {Technische Universit{\"a}t Berlin}, address = {Berlin}, pages = {178}, school = {Technische Universit{\"a}t Berlin}, year = {2018}, language = {en} } @phdthesis{Nebl2021, author = {Nebl, Christoph}, title = {Electrical characterisation of lithium-ion cells above their operating range}, publisher = {RMIT University}, address = {Melbourne}, pages = {XIV, 122}, school = {RMIT University}, year = {2021}, language = {en} } @phdthesis{Delooz2023, author = {Delooz, Quentin}, title = {Sensor Data Sharing in V2X Communications: Protocol Design and Performance Optimization of Collective Perception}, publisher = {Halmstad University Press}, address = {Halmstad}, url = {http://nbn-resolving.de/urn:nbn:se:hh:diva-50463}, pages = {xiv, 44}, school = {Halmstad University}, year = {2023}, language = {en} }