Masters by Research/PhD Studentship: Sparse Clustering Based IoT Anomaly Intrusion Detection Using Information Theoretic Feature Selection

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This project is one of only four winners in the security and privacy theme of Samsung's highly competitive global academic research collaboration programme for 2017. The project proposes a next generation anomaly intrusion detection and prevention system (IDPS) for IoT-enabled devices to strengthen cyber-security on everyday items such as IP cams, smart home routers, smart watches, temperature sensors connected to IoT hubs/smart-home routers via personal area network (PAN) or enterprise wide area network (WAN).

The research will focus on:

  • Exploring the uses of information theory inspired machine learning algorithms to model complex IoT-based intrusions, particularly where there are significant constraints in computational resources.

  • Designing and developing algorithms that can capture anomalous behaviours and deploying them to identify threats on IoT networks.

The student will be based at the Shrivenham Campus (Defence Academy of the UK). This work is industrially sponsored with a generous tax-free stipend, international and national conference attendance and dedicated fund for a workstation. The student will be expected to both author papers and present at relevant conferences. The student will also be given the opportunity to attend top-tier IEEE/ACM conferences.

This studentship will have opportunities to work with Samsung’s world-class researchers.

At a glance

  • Application deadline23 Mar 2018
  • Award type(s)PhD, MSc by Research
  • Start date01 Jun 2018
  • Duration of award1 Year with possibility to upgrade to 3 year PhD
  • EligibilityEU, UK
  • Reference numberPHDEWIC01

Entry requirements

Applicants should have a first or second class UK honours degree or equivalent in a related discipline, such as computer science, engineering or related degree. The ideal candidate should have good programming skills and some practical experience of completing an applied project. An interest in cyber security, data analytics, machine learning and information theory would be beneficial. The candidate should be self-motivated and have good communication skills (including written communication e.g. documenting research or work).

If English is not your first language, you should have an IELTS score of 6.5 or equivalent.


Sponsored by Samsung, this studentship will provide a bursary of up to £15,000 p.a. (tax free) plus fees* for one year (possibility for 3 years PhD upgrade subject to agreement by the industry partner)

This position is open to all, but please note that only UK/EU tuition fees will be covered by the scholarship. International students are welcome to apply, but will be required to pay the difference between the UK/EU fee level and the international fee level.

Cranfield Doctoral Network

Research students at Cranfield benefit from being part of a dynamic, focused and professional study environment and all become valued members of the Cranfield Doctoral Network. This Network brings together both research students and staff, providing a platform for our researchers to share ideas, identify opportunities for collaboration and create smaller communities of practice.  It aims to encourage an effective and vibrant research culture, founded upon the diversity of activities and knowledge. A tailored programme of seminars and events alongside our Doctoral Researchers Core Development programme (transferable skills training), provide those studying a research degree with a wealth of social and networking opportunities.

How to apply

Candidates interested in this post should provide a cover letter containing a concise description of an applied project in which they have been involved along with their CV to Dr Paul Yoo or Dr Taufiq Asyhari by email.

For further information please contact: Dr Paul Yoo ( or Dr Taufiq Asyhari ( Reference no.  PHDEWIC01


For further information contact us today:

Cranfield Doctoral Network, School of Defence and Security,
T: 44 (0) 1793 785008