Fully Funded PhDs in a Range of Crime and Security Topics Including Cybersecurity, IoT, Imaging

University College London

The following FIVE studentships are available for a September 2018 start for our four-year programme (MRes+PhD). Three of these studentships are for ‘pre-set topics’, and two are for ‘open topics’ – details are below.

Eligibility: These awards are open to Home/EU fee paying students only.

What the awards cover: Each award covers full stipend of approx. £16,500 per annum for four years, full Home/EU fees, plus conference funds of £1,000 per annum.

Deadline for application: 31st May 2018 (However, we advise that you apply early, as we will be awarding the studentships as soon as we identify excellent candidates.)

How to apply: All applications must be made through the usual UCL SECReT (UCL Security Science Doctoral Centre) application procedure. Please read carefully: www.ucl.ac.uk/secret/fees-funding/course/apply

Come to our OPEN EVENING on 21st March 2018 6pm at UCL, London 

The open evening includes presentations by programme leaders about the vision and goals of the centre, our areas of research, and the wider activities that students will participate in as part of their training, and also essential tips on the application and scholarship award process.

To register for the open evening click here: www.eventbrite.co.uk/e/open-evening-for-crime-and-security-phds-at-ucl-secret-tickets-43170938439


Open topics:

Of the FIVE studentships available THREE will cover the topics set out below in the pre-set topics section. However, if you have a topic that you would like to explore that is not covered by the pre-set topics, you may apply for one of our TWO ‘open topic’ studentships. In this case please detail your proposed research topic in your application – note that your topic must fit with the future crimes vision and agenda of the Dawes Centre for Future Crime. You can learn more about the centre here: www.ucl.ac.uk/dawes-future-crime

If you would like to check the suitability of your proposed topic before submitting an application please email Professor Shane Johnson: shane.johnson@ucl.ac.uk

Pre-set topics:

Guarding against Adversarial Perturbation in Automated Security Algorithms

Deep Learning algorithms can detect and classify people, activities and objects in images with performance at human-level. These methods are finding their way into consumer products. They also offer great potential in security applications for example in person verification at checkpoints, suspect-finding in video, discovering harmful content on the internet, and detecting threats in bags and parcels. However, they have a curious vulnerability ('adversarial perturbation'), which while harmless for consumer applications offers a potential exploit for determined adversaries in the security realm. An adversarial perturbation is a very small, but very precise, change to the image input into a classifier that causes the classifier output to dramatically change. For example, with just the right perturbation an image of a cat can be mis-classified as a dog, while still looking like a cat to a human viewer. In the context of security this method could allow an adversary to, for example: alter harmful video content to escape detection by automated methods; conceal threats in bags; or maliciously 'place' targeted individuals into pornographic content, so far as face-based search algorithms are concerned. At present there is no known fix for adversarial perturbations. Can this problem be fixed, or is it an issue that we need to learn to live with and safeguard against in other ways? This PhD will address this problem before adversaries develop the sophistication to exploit it.

Possible supervisor(s) at UCL: Dr Lewis Griffin. Contact: l.griffin@cs.ucl.ac.uk

Low energy X-ray backscatter imaging for non-destructive evidence harvesting

When X-rays interact with a material one process that can occur is inelastic or Compton scattering. The X-ray loses some energy to an electron in the material and is caused to change direction.  Sometimes the X-ray will be scatter in the backwards direction. The probability of an X-ray scattering in a direction which is useful for backscatter imaging is dependent on the incident X-ray energy and the density of the material it interacts with. This relationship is described by the Klein-Nishina equation but, importantly, the probability of an X-ray scattering in the backwards direction is greatest for low energy X-rays interacting with low density materials (i.e. organics). This makes X-ray backscatter imaging a useful technique for investigating surface contaminates (e.g. oils, biological material, drugs, explosives, etc.) and features. For example, it may be possible to build a single-sided imaging system that could be used as and non-contact, non-destructive tool for recording fingerprints, particularly on surfaces that are difficult with current techniques (e.g. textured surfaces).

This project is about investigating the technical trade-offs associated with X-ray backscatter imaging for the purposed of scene-of-crime evidence collecting. The student will be expected to design and build a lab based experimental setup to develop an understanding of how the X-ray generator/detector geometry and settings can be adjusted to optimise the technique. The project will also investigate how information is affected by background substrate, X-ray energy and detector characteristics.

Possible supervisor(s) at UCL: Prof Robert Speller. Contact: r.speller@ucl.ac.uk

Future Crime and the Internet of Things (IoT)

The Internet of Things (IoT) refers to the network of objects containing embedded electronic systems that permit them to collect and process data and to communicate wirelessly with other objects in the network. The IoT includes household products and control systems (e.g. fridges, thermostats, and security systems), building management systems (e.g. heating, ventilation, and access control), medical equipment (patient monitors, information), and retail equipment (point of sale terminals, security systems). The IoT makes our lives easier and systems more efficient but also generates potential opportunities for crime. The aim of this PhD is to understand and suggest approaches to combatting vulnerabilities of the IoT, giving special attention to the ‘everyday’ crimes experienced by the general population that are typically overlooked in cyber security research.

Possible supervisor(s) at UCL: Prof Steve Hailes. Contact: s.hailes@ucl.ac.uk

About the funders:

The Dawes Centre for Future Crime at UCL was established in 2016 with a £3.7M grant from the Dawes Trust. It has the broad vision of completing cutting-edge, application-focused research designed to meet the challenges of the changing nature of crime. Research aims to both forecast the nature and spread of future crime opportunities, and to propose methods for tackling them effectively before they become established.

UCL SECReT is the £17m international centre for PhD training in security and crime science at University College London, the first centre of its kind in Europe. We offer the most comprehensive integrated PhD programme for students wishing to pursue multidisciplinary security or crime-related research degrees.

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