Dr Thomas Tan

Biography	Dr Zhiyuan (Thomas) Tan is an Associate Professor in the School Of Computing, Engineering and The Built Environment at the Edinburgh Napier University (ENU). He holds a BEng degree (2005) with high distinction from the North-eastern University, China, and an MEng degree (2008) from the Beijing University of Technology, China. He was awarded a PhD degree in Computer Systems by the University of Technology Sydney (UTS), Australia in 2014. Prior to joining ENU in 2016, Dr Tan held different research positions at three research-intensive universities, respectively. He was a Postdoctoral Researcher in Cybersecurity at the University of Twente (UT), the Netherlands from 2014 to 2016; a Research Associate at the University of Technology, Sydney (UTS), Australia in 2014; and a Senior Research Assistant at La Trobe University, Australia in 2013. Dr Tan has a strong research background, and his current research interests include cybersecurity, machine learning, data analytics, virtualisation and cyber-physical system. Dr Tan has received AU$27,800 funding from Commonwealth Scientific and Industrial Research Organisation (CSIRO) and UTS, £73,564 funding from Carnegie Trust for the Universities of Scotland, £12,000 funding from the Royal Society, £29,703 funding from ENU Development Trust, £10,759 funding from the Scottish Informatics & Computer Science Alliance, as well as £13,987 funding from ENU. Over the past nine years, he also has participated in other network security research projects funded by CSIRO, the Minster of Education (Oman), and ITEA2-/CATRENE. These projects have contributed highly efficient solutions for 1) detecting, classifying and defending malicious activity and intrusion in an entire network as well as systems providing critical services, 2) furthering the cutting edge in practical security monitoring, 3) developing secure data and service architectures, 4) protecting security and privacy of vehicular networks and 5) security and privacy of machine learning. Based on the outcomes of these projects, Dr Tan has published over 110 quality scholarly articles. His most recent research achievements have been published in highly-cited IEEE Transactions and Elsevier journals as well as premier international conferences. His research contribution to cybersecurity is internationally recognised. He was featured in Stanford University's 2021, 2022, 2023 and 2024 lists of the world’s Top 2% scientists. He has earned various research awards, including a National Research Award 2017 from the Research Council of the Sultanate of Oman, three Best Paper Awards, and a Kaspersky Lab’s Annual Student Cyber Security Conference Finalist Award, over the past years. Besides, Dr Tan has played various chair roles in international workshops and conferences and has served international journals (including IEEE Transactions on Reliability (ISSN: 0018-9529), IEEE Open Journal of the Computer Society (ISSN: 0740-7459) and Journal of Ambient Intelligence and Humanized Computing (ISSN: 1868-5137)) as an editorial board member or as an associate editor. Dr Tan has organised Special Issues for international journals. Dr Tan has also been invited to serve as a technical program committee member of major international conferences and a reviewer for prestigious journals. In addition, Dr Tan has been involved in supervising Research Master's and PhD students since he accomplished his PhD thesis in 2013 and has been entitled to independently supervise research student projects since 2016. Over the past 10 years, Dr Tan has mentored and supervised 13 PhD students. By far, 8 PhD students have successfully accomplished their studies under his mentoring/supervision. He received an Honourable Mention in SICSA Supervisor of the Year 2019 Award. Dr Tan is now RECRUITING highly self-motivated Ph.D. students, who are expected to conduct challenging research on Network Security - Machine unlearning in the context of Federated Learning and Large Language Models (LLM) - Adversarial machine learning for Anomaly/Malware detection - Virtualisation security based on non-parametric behaviour modelling - Knowledge transfer (Transfer Machine Learning) in cyber-security problems - IoT Security with focus on Cloud and Edge computing security issues - Security and Privacy of Machine Learning - Security and Privacy of Vehicular Network and Electric Vehicle charging infrastructure.
Research Interests	big data, cloud computing, cyber security, feature extraction, feature selection, intrusion detection, machine learning, 5G, virtualization
Teaching and Learning	Operating Systems, Network Security, Networking, Incident Response & Malware Analysis
ResearcherID	O-4426-2014
Scopus Author ID	35148534800
PhD Supervision Availability	Yes
PhD Topics	Machine Learning based Anomaly Detection/Malware Detection/Virtual Machine Introspection, Cyber-bullying Detection in Social Network, Remote Device Discovery, Transfer Machine Learning for Cyberscurity, Large-scale Feature Selection