Lecturer, University of East Anglia.

Dr Oli Buckley is a Lecturer in the School of Computing Sciences at the University of East Anglia. Prior to his role at UEA Oli has worked at Cranfield University as part of Cranfield Defence and Security and at the University of Oxford’s Cyber Security Centre.

He has current research projects that focus on trust, fairness and justice in algorithmic decision making and the perceptions of privacy, trust and disclosure to conversational agents.

Oli’s other research interests include: the human-aspects of cyber security, behavioural biometrics, user identification and identity, understanding and detecting insider threats, security visualisation, data leakage and digital identity.

Personal webpage

Recent Publications

  • Reconstructing what you said: Text Inference using Smartphone Motion Hodges, D. & Buckley, O. 1 Apr 2019 In: IEEE Transactions on Mobile Computing.18, 4, p. 947-959
  • Deconstructing who you play: Character choice in Online Gaming Hodges, D. & Buckley, O. Aug 2018 In: Entertainment Computing. 27, p. 170-178 9
  • Behind the scenes: a cross-country study into third-party website referencing and the online advertising ecosystem Nurse, J. R. C. & Buckley, O. 2 Nov 2017 In: Human-Centric Computing and Information Sciences. 7, 1, 40
  • Automated Insider Threat Detection System Using User and Role-Based Profile Assessment Legg, P. A., Buckley, O., Goldsmith, M. & Creese, S. Jun 2017 In: IEEE Systems Journal. 11, 2, p. 503-512 10 p.



CLICKA: Collecting and leveraging identity cues with keystroke dynamics

The way in which IT systems are usually secured is through the use of username and password pairs. However, these credentials are all too easily lost, stolen or compromised. The use of behavioural biometrics can be used to supplement these credentials to provide a greater level of assurance in the identity of an authenticated user. However, user behaviours can also be used to ascertain other identifiable information about an individual. In this paper we build upon the notion of keystroke dynamics (the analysis of typing behaviours) to infer an anonymous user’s name and predict their native language. This work found that there is a discernible difference in the ranking of bigrams (based on their timing) contained within the name of a user and those that are not. As a result we propose that individuals will reliably type information they are familiar with in a discernibly different way. In our study we found that it should be possible to identify approximately a third of the bigrams forming an anonymous users name purely from how (not what) they type.

Authors: Oli Buckley, Duncan Hodges

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