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

Abstract

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.

The Automotive BlackBox: Towards a Standardization of Automotive Digital Forensics

“…An immense amount of data is continuously transmitted, e.g., with safety-critical systems, including brakes, steering, and acceleration. In previous work, we identified 16 categories of forensically relevant data and stated the required security properties for the data [3]. However, modern vehicles only log a fraction of forensically relevant data, and manual approaches are often used to manage the data.…”

“…In [3], four main stakeholders are identified for ADF, namely: Law Enforcement (LE), Vehicle Manufacture (VM), Vehicle Drivers (VD), and Insurance Companies (IC). LE refers to, e.g., the police and related legal systems.…”

“…We highlight challenges, identify forensic components, and propose a standard data format, techniques, goals, and requirements in an architectural automotive context, considering current and upcoming regulations and standards. Based on our previous work, a systematic literature review of the area [3], our contributions are novel and relevant for automotive digital forensics investigations.…”

Cybersecurity and Forensics in Connected Autonomous Vehicles: A Review of the State-of-the-Art

“…The purpose of CAVs forensics is to acquire data and develop a timetable of events to provide courts with accurate information on criminal activity or accidents. Vehicle forensics is an emerging field, and the authors of [133], [134] explored this new branch of forensics by performing forensics on a real testbed. Their work demonstrated that although standardized guidelines on CAV forensics are not yet available, there are several existing branches of forensic and software testing techniques that could play a key role in CAVs after a crash investigation.…”

Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather

“…Object detection is a critical component of autonomous driving, which relies on computer vision and artificial intelligence techniques to understand driving scenarios [2], [14]. However, the foggy and rainy weather conditions make the understanding of camera images particularly difficult, which poses challenges to the camera based object detection system installed on the intelligent vehicles [15]- [18].…”