Thesis Work: Cybersecurity and Digital Forensics

About this opportunity - Background

The automotive industry faces growing cybersecurity challenges as vehicles become increasingly connected and autonomous. Modern cars are equipped with complex electronic systems and are susceptible to cyber threats. Enhancing detection and forensic capabilities within automotive systems is essential to mitigate these threats. 

This project aims to identify and analyze state-of-the-art methods, tools, and mechanisms for enhancing detection and forensic capabilities within the automotive sector.

Scope of the thesis work

Integrating digital technologies into automotive systems has increased the risk of cyberattacks, which can compromise vehicle safety and security. There is a critical need for enhanced detection mechanisms and forensic capabilities to effectively identify and respond to such threats. This project aims to explore advanced methods for improving cybersecurity and digital forensics in the automotive sector. It will build upon prior research efforts [1-3], with a focus on the current state-of-the-art in digital forensics. However, in contrast to [1-3], this thesis will expand on the research presented in [1-3] by incorporating related fields such as the Internet of Things (IoT), Cyber-Physical Systems (CPS), avionics, and railway systems, with a particular emphasis on their applicability to the automotive sector. A gap analysis of the current automotive landscape, along with a mapping of automotive relevance to other investigated areas and future guidelines, will also be provided.

Objectives

• To review state-of-the-art digital forensics methods in adjacent areas and their implications for detection and forensic analysis within automotive.
• To conduct a thorough literature review of technical reports, standards, and regulations relevant to automotive digital forensics to identify existing gaps.
• To explore and practically validate selected digital forensics techniques from the literature through a simplified use case or prototype, to assess feasibility for automotive contexts.
• To provide a state-of-the-art report that includes analysis and guidelines for implementing advanced detection and forensics methods in automotive systems.

Expected Outcomes 
A state-of-the-art report: 
• Highlighting the findings from the literature review and analysis, including guidelines for enhancing detection and forensics capabilities in automotive systems. 
• Identify gaps in existing automotive cybersecurity practices and suggest future research directions.
• Provide recommendations for implementing techniques to improve detection and forensics in real-world automotive cybersecurity scenarios.
• A small-scale proof of concept (POC) or use case, e.g., based on open-source tools or alternative hardware, demonstrating and evaluating the applicability of one or more digital‑forensics methods in an automotive‑like setting.

Related Work:
[1] K. Strandberg, N. Nowdehi and T. Olovsson, "A Systematic Literature Review on Automotive Digital Forensics: Challenges, Technical Solutions and Data Collection," IEEE Transactions on Intelligent Vehicles, vol. 8, no. 2, pp. 1350-1367, Feb 2023. 
[2] K. Strandberg, U. Arnljung and T. Olovsson, "The Automotive BlackBox: Towards a Standardization of Automotive Digital Forensics," in 2023 IEEE International Workshop on Information Forensics and Security (WIFS), Nurnberg, Germany, 2023
[3] K. Strandberg and Mohamed Eldefrawy, “Advances in Automotive Digital Forensics: Recent Trends and Future Directions,” Availability, Reliability and Security. ARES 2025, Ghent, Belgium. Lecture Notes in Computer Science, vol 15993. Springer

What you'll bring

• Two MSc. students in their final year with a background in Computer Science 
• Knowledge of cybersecurity and digital forensics
• English language Fluency, written and spoken

Duration

•    The work will start at the beginning of 2026
•    30 academic credits in agreement with your Thesis Advisor at the University
•    This thesis is to be conducted by 2 Students working in pair.