Data Sources for Information Extraction in Automotive Forensics

Attenberger, Andreas

doi:10.1007/978-3-030-45096-0_17

Cited by 1 publication

(4 citation statements)

References 14 publications

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“…In contrast, data Example of data and source External devices. [24]- [26], [31], [32], [36], [63], [65], [68]- [70], [73], [74], [77], [81], [82], [112] C.I.A.N.P. LE.VM.VD.…”

Section: Discussionmentioning

confidence: 99%

“…In the remainder of this section, we have picked the most representative studies for each category and refer to Table I and II for the complete list. A. Attenberger [63] presents considerations for data generated in vehicles and categorizes these into two groups, namely, front end and back end. The former are vehicle electronics inside the vehicle, such as the infotainment module, and the latter, outside the vehicle, such as the cloud.…”

Section: B Categorization Of Papersmentioning

confidence: 99%

“…Data from events can be found inside the vehicle, e.g., logs, and outside the vehicle, e.g., traffic data. Aligned with [63], machine learning is mentioned as important in future crime investigation. Automatic approaches to managing large volumes of data, alerting when data matches previous crimes, and predicting and finding preparations patterns for potential future crimes are identified as important in future research.…”

Section: B Categorization Of Papersmentioning

confidence: 99%

“…In [63], Attenberger et al suggest Machine Learning to automate data handling and driver identification. Supervised classification can be used to train an algorithm into classes of data connected to individual drivers.…”

Section: B Categorization Of Papersmentioning

confidence: 99%

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A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection

Strandberg

Nowdehi

Olovsson

2023

IEEE Trans. Intell. Veh.

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A modern vehicle has a complex internal architecture and is wirelessly connected to the Internet, other vehicles, and the infrastructure. The risk of cyber attacks and other criminal incidents along with recent road accidents caused by autonomous vehicles calls for more research on automotive digital forensics. Failures in automated driving functions can be caused by hardware and software failures and cyber security issues. Thus, it is imperative to be able to determine and investigate the cause of these failures, something which requires trustable data. However, automotive digital forensics is a relatively new field for the automotive where most existing self-monitoring and diagnostic systems in vehicles only monitor safety-related events. To the best of our knowledge, our work is the first systematic literature review on the current research within this field. We identify and assess over 300 papers published between 2006 -2021 and further map the relevant papers to different categories based on identified focus areas to give a comprehensive overview of the forensics field and the related research activities. Moreover, we identify forensically relevant data from the literature, link the data to categories, and further map them to required security properties and potential stakeholders. Our categorization makes it easy for practitioners and researchers to quickly find relevant work within a particular sub-field of digital forensics. We believe our contributions can guide digital forensic investigations in automotive and similar areas, such as cyber-physical systems and smart cities, facilitate further research, and serve as a guideline for engineers implementing forensics mechanisms.

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“…In contrast, data Example of data and source External devices. [24]- [26], [31], [32], [36], [63], [65], [68]- [70], [73], [74], [77], [81], [82], [112] C.I.A.N.P. LE.VM.VD.…”

Section: Discussionmentioning

confidence: 99%

Section: B Categorization Of Papersmentioning

confidence: 99%

Section: B Categorization Of Papersmentioning

confidence: 99%

Section: B Categorization Of Papersmentioning

confidence: 99%

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