Oli Buckley
Before appointed at Loughborough University, Oli Buckley was a Professor of Cyber Security at the University of East Anglia (UEA), where he established the Cyber Security, Privacy and Trust Lab.
His research focuses on the human aspects of cyber security, insider threat, privacy and trust. Oli has received in excess of £2.5m funding from a range of funders including EPSRC, CREST, Innovate UK and other government sources.
His recent projects include user identification and behavioural biometrics with keystroke dynamics, social justice in algorithmic decision making and the use of chatbots to improve trust and sensitive disclosures.
Oli's teaching is focused on cyber security and related subjects and he is the Course Director for the MSc Cyber Security.
Prior to joining UEA Oli worked as a Lecturer in the Information Operations group at Cranfield University, a Researcher at the University of Oxford Cybersecurity Centre and as a software engineer in industry.
Projects
Articles
Academic Publications
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.
https://doi.org/10.1016/j.cose.2022.102780
Projects
Articles
Academic Publications
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.