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Protecting Vehicles from Cyber Attacks

  • Full or part time
  • Application Deadline
    Sunday, December 22, 2019
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Applications are invited for a fully-funded Research Studentship (PhD) to carry out research at Robert Gordon University, Aberdeen, United Kingdom, under the supervision of Dr Harsha Kalutarage and his team.

Proposed Research
The design of automobiles has for years been completely separated from the way in which they exchange information within the vehicle. The connectedness of these systems now makes it possible to communicate information with computers and other Internet of Things (IoTs) in the external world. An average modern vehicle contains a million lines of software code, and up to 100 electronic control units (ECUs) interact in multiple networks (eg, CAN, LIN) to facilitate this connectivity and control the other functions of a vehicle (eg. braking). As a result, vehicle’s cyber risks have grown significantly over the past few years; a hacker compromising the braking or steering system of a vehicle may even result in the driver or passenger losing their lives. Illegitimately accessing and modifying data in a vehicle therefore is not only a security issue but also a safety issue. Hence the security of connected and autonomous vehicles is a major concern for OEMs and car manufacturers, who are now seeking for methods to secure their products against cyber attacks.

The proposed research aims at developing a practically deployable cyber security solution to the modern automobiles, which would be based on advanced Artificial Intelligence (AI) technologies. During the research program, the student will work with the Industrial partner HORIBA MIRA Ltd. and leverage the existing expertise and collaboration between the academic and industrial partner in this research area. S/he will spend some time periodically at HORIBA MIRA’s state-of-the-art research facility in Nuneaton (Coventry), under the supervision of Dr Madeline Cheah as the industrial supervisor.

Person Specification

Bachelor degree in Computer Science (UK equivalent 1st or 2nd upper) or related disciplines such as Information Security, Cyber Security, Computer Networks, Mathematics, Statistics, Data Science, Artificial Intelligence or Electronics and Electrical Engineering.
Strong interest in Cyber Security and Artificial Intelligence
Programming experience (any language eg. Java, C, R, Python)
Good analytical skills - knowledge of foundations of computer science, ability to think independently
Strong oral and written communication skills, in both plain English and academic languages, for publication in relevant journals and presentation at conferences

Master’s degree in above disciplines (UK equivalent of Merit or above)
Good understanding of the principles of computer security
Good understanding of the Machine Learning product development life cycle (eg. Team Data Science Process, CRISP-DM)
Experience in designing and developing AI-based products/systems
Relevant industry qualifications such as CISSP, CEH and OSCP would be an added advantage to the proposed study

Applications should be emailed to Kate Lines at . The applications should consist of a covering letter or personal statement of interest, and a CV. Further information such as passport details or transcripts may be requested during the short-listing stage. Applicants will be contacted for interview and may be asked to complete a short practical task. Interviews will take place at RGU in Aberdeen in the week commencing 6th January 2020.

Enquiries can be emailed to Kate Lines at and will be forwarded to Dr Harsha Kalutarage if technical in nature.

Funding Notes

Duration and Funding
The duration of project will be up to 36 months, commencing in February 2020. The studentship covers both tuition fees (at Home/EU or International level) and a tax-free stipend of £15,000 per annum.

How good is research at Robert Gordon University in Computer Science and Informatics?

FTE Category A staff submitted: 18.75

Research output data provided by the Research Excellence Framework (REF)

Click here to see the results for all UK universities

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