| Fog computing security: a review of current applications and security solutions S Khan, S Parkinson, Y Qin Journal of Cloud Computing 6, 1-22, 2017 | 501 | 2017 |
| Biometric systems utilising health data from wearable devices: applications and future challenges in computer security S Khan, S Parkinson, L Grant, N Liu, S Mcguire ACM Computing Surveys (CSUR) 53 (4), 1-29, 2020 | 96 | 2020 |
| Review into state of the art of vulnerability assessment using artificial intelligence S Khan, S Parkinson Guide to Vulnerability Analysis for Computer Networks and Systems: An …, 2018 | 56 | 2018 |
| Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach S Khan, S Parkinson Expert Systems with Applications 113, 116-127, 2018 | 37 | 2018 |
| A survey on the detection and impacts of deepfakes in visual, audio, and textual formats R Mubarak, T Alsboui, O Alshaikh, I Inuwa-Dutse, S Khan, S Parkinson Ieee Access 11, 144497-144529, 2023 | 35 | 2023 |
| A survey on empirical security analysis of access-control systems: a real-world perspective S Parkinson, S Khan ACM Computing Surveys 55 (6), 1-28, 2022 | 34 | 2022 |
| Password policy characteristics and keystroke biometric authentication S Parkinson, S Khan, A Crampton, Q Xu, W Xie, N Liu, K Dakin IET Biometrics 10 (2), 163-178, 2021 | 29 | 2021 |
| Discovering and utilising expert knowledge from security event logs S Khan, S Parkinson Journal of Information Security and Applications 48, 22, 2019 | 27 | 2019 |
| Built environment attributes and crime: an automated machine learning approach K Dakin, W Xie, S Parkinson, S Khan, L Monchuk, K Pease Crime Science 9, 1-17, 2020 | 24 | 2020 |
| Identifying irregularities in security event logs through an object-based Chi-squared test of independence S Parkinson, S Khan Journal of Information Security and Applications 40, 52-62, 2018 | 18 | 2018 |
| Towards automated vulnerability assessment S Khan, S Parkinson | 16 | 2017 |
| Exploring perceptions of decision-makers and specialists in defensive machine learning cybersecurity applications: The need for a standardised approach O Alshaikh, S Parkinson, S Khan Computers & Security 139, 103694, 2024 | 15 | 2024 |
| Identifying high-risk over-entitlement in access control policies using fuzzy logic S Parkinson, S Khan Cybersecurity 5 (1), 1-17, 2022 | 11 | 2022 |
| An empirical analysis of keystroke dynamics in passwords: A longitudinal study S Parkinson, S Khan, AM Badea, A Crampton, N Liu, Q Xu IET Biometrics 12 (1), 25-37, 2023 | 10 | 2023 |
| An Unsupervised Approach for the Detection of Zero-Day DDoS Attacks in IoT Networks M Roopak, S Parkinson, GY Tian, Y Ran, S Khan, B Chandrasekaran Authorea Preprints, 2024 | 9 | 2024 |
| Causal connections mining within security event logs S Khan, S Parkinson Proceedings of the 9th Knowledge Capture Conference, 1-4, 2017 | 9 | 2017 |
| Context-based irregular activity detection in event logs for forensic investigations: An itemset mining approach S Khan, S Parkinson, C Murphy Expert Systems with Applications 233, 120991, 2023 | 8 | 2023 |
| Comparison of deep learning classification models for facial image age estimation in digital forensic investigations M Roopak, S Khan, S Parkinson, R Armitage Forensic Science International: Digital Investigation 47, 301637, 2023 | 7 | 2023 |
| A multi-layered cloud protection framework S Khan, S Parkinson, A Crampton Companion Proceedings of the10th International Conference on Utility and …, 2017 | 7 | 2017 |
| Creeper: a tool for detecting permission creep in file system access controls S Parkinson, S Khan, J Bray, D Shreef Cybersecurity 2 (1), 14, 2019 | 6 | 2019 |
Simon ParkinsonProfessor, University of Huddersfield & UK Government Cyber Security Advisory Board MemberVahvistettu sähköpostiosoite verkkotunnuksessa hud.ac.uk