Maritime Cyber Risk assessment and mitigation using Artificial Intelligence

This project is now closed. The details below are for information purposes only.
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Applications are invited for a 3 year PhD to commence in October 2020 or February 2021.

The PhD will be based in the Faculty of Technology, and will be supervised by Dr Stavros Shiaeles and Dr Fudong Li.

Fees and funding

Funding availability: Self-funded PhD students only. 

PhD full-time and part-time courses are eligible for the UK Government Doctoral Loan (UK and EU students only).

The need to reduce operational costs and delivery times has led the shipping industry to seek technological solutions to automate operations (Tovey, 2016) without taking in consideration cyber-security. On the other hand, autonomy is disrupting the maritime sector in the same way as smart phones did over ten years ago. The maritime industry controls over 90% of the world trade and is estimated at $183.3 billion. Finland is currently piloting the autonomous ship (an unmanned surface
vehicle (USV)), whereas, three other organizations are aiming to produce their remotely operated vessels by 2020 and ocean-going ships for maritime transport by 2030 [1]. These unmanned ships aim to increase safety of operations at the sea, reduce fuel consumption, and transform the work roles in the maritime domain. Maritime cyber threats are, as for most systems, a combination of human factors and technology, therefore, a system that would allow vessel owners to assess the level of cyber security risk, resulting from behaviors of staff, their IT systems and potential attacks is of great importance.

Currently, there is no method to provide a real-time risk assessment of cyber threats on-board vessels, so the aim of this project is utilise research conducted in [2,3,4), investigate PLC and SCADA technologies and protocols used in maritime sector and developed a Cyber Security Risk Assessment and Mitigation framework that will increase security and resilience of the shipping industry against cyber-attacks by monitoring human behaviours and IT system, minimising potential risk based on a continuous analysis of real time information.

References:

[1] Rolls-Royce, “Autonomous ships: The next step,” White Paper, Available: http://www.rolls-royce.com/~/media/Files/R/Rolls-Royce/ documents/customers/marine/ship-intel/rr-ship-intel-aawa-8pg.pdf [Last accessed: 21 st Feb. 2019]

[2] Shire, R., Shiaeles, S., Bendiab, K., Ghita, B., & Kolokotronis, N. (2019). Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation. In Internet of Things, Smart Spaces, and Next Generation Networks and Systems (pp.65–76). Springer, Cham. DOI: 10.1007/978-3-030-30859-9_6

[3] Shiaeles, S., Kolokotronis, N., & Bellini, E. (2019, July). IoT vulnerability data crawling and analysis. In 2019 IEEE World Congress on Services (SERVICES) (Vol. 2642, pp. 78–83). IEEE. DOI:10.1109/SERVICES.2019.00028

[4] Kolokotronis, N., Brotsis, S., Germanos, G., Vassilakis, C., & Shiaeles, S. (2019, July). On Blockchain Architectures for Trust-Based Collaborative Intrusion Detection. In 2019 IEEE World Congress on Services (SERVICES) (Vol. 2642, pp. 21–28). IEEE. DOI: 10.1109/SERVICES.2019.00019

How to apply

We’d encourage you to contact Dr Stavros Shiaeles (stavros.shiaeles@port.ac.uk) to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this PhD opportunity you must quote project code COMP5310220 when applying.

October start

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February start

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