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Tingting Li  BEng(Hons), MSc, PhD, FHEA

Dr Tingting Li

BEng(Hons), MSc, PhD, FHEA

Lecturer

School of Computer Science and Informatics

Email
LiT29@cardiff.ac.uk
Telephone
+44 29208 79153
Campuses
Abacws, Room 5.23, Senghennydd Road, Cathays, Cardiff, CF24 4AG
Users
Available for postgraduate supervision

Overview

NEWS (02/2023): I have a funded PhD position on Cyber Resilience of Cyber-Physical Systems. Please see details here: Improving Cyber Resilience of Cyber-Physical Systems by Dynamic Diversification

Dr Tingting Li is currently a Lecturer (Assistant Professor) in Cyber Security at Cardiff University. Her research interests primarily lie in AI for cyber security, Automated Cyber Defence and Diversification/Deception strategies. Her expertise extends to defend Cyber-Physical Systems, ICS/SCADA and Autonomous Systems (autonomous vehicles, Robots). She also explores Symbolic AI for knowledge representation and reasoning.

Prior to joining Cardiff, she was a Postdoctoral Research Associate at the Institute for Security Science & TechnologyImperial College London. She obtained her PhD degree in Artificial Intelligence from the University of Bath. She also received her MSc degree in Computing (Imperial College London) and her bachelor's degree in Information Security (Xidian University, China).

Please visit her Personal Page for more details.

Publication

2024

2020

2017

2016

  • Fielder, A., Li, T. and Hankin, C. 2016. Defense-in-depth vs. critical component defense for industrial control systems. Presented at: 4th International Symposium for ICS & SCADA Cyber Security Research 2016 (ICS-CSR 2016), Belfast, Ireland, United Kingdom, 23-25 August 2016ICS-CSR '16: Proceedings of the 4th International Symposium for ICS & SCADA Cyber Security Research 2016. BCS Learning & Development Ltd. pp. 1-10., (10.14236/ewic/ICS2016.1)
  • Fielder, A., Li, T. and Hankin, C. 2016. Modelling cost-effectiveness of defenses in industrial control systems. Presented at: International Conference on Computer Safety, Reliability, and Security (SafeComp 2016), Trondheim, Norway, 20-23 September 2016 Presented at Skavhaug, A., Guiochet, J. and Bitsch, F. eds.Computer Safety, Reliability, and Security: 35th International Conference, SAFECOMP 2016, Trondheim, Norway, September 21-23, 2016, Proceedings, Vol. 9922. Lecture Notes in Computer Science series and Programming and Software Engineering series Springer Verlag pp. 187-200., (10.1007/978-3-319-45477-1_15)
  • Padget, J., ElDeen Elakehal, E., Li, T. and De Vos, M. 2016. InstAL: An institutional action language. In: Social Coordination Frameworks for Social Technical Systems., Vol. 30. Law, Governance and Technology Series, pp. 101-124., (10.1007/978-3-319-33570-4_6)

2015

  • King, T. C., Li, T., Vos, M. D., Dignum, V., Jonker, C. M., Padget, J. and van Riemsdijk, M. B. 2015. A framework for institutions governing institutions. Presented at: International Conference on Autonomous Agents and Multiagent Systems (AAMAS '15), Istanbul, Turkey, 4-8 May 2015AAMAS '15: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems. Richmond, SC, USA: International Foundation for Autonomous Agents and Multiagent Systems pp. 473-481., (10.5555/2772879.2772940)

2013

Adrannau llyfrau

Cynadleddau

Erthyglau

Research

My research interests primarily lie in AI for cyber security, Automated Cyber Defence and Diversification/Deception strategies. My expertise extends to defend Cyber-Physical Systems, ICS/SCADA and Autonomous Systems (autonomous vehicles, Robots). I also explore Symbolic AI for knowledge representation and reasoning.

For the list of my publications, please visit my Google Scholar page.

I have been involved in several grants from a variety of sources. Selective grants are listed below:

Diversity-by-design Quantifying vulnerability Similarity of Interconnected Networks

Investigator: Tingting Li (PI) and Pete Burnap
Timeline: 2021-2022
Project value (funder): £142K (GCHQ/NCSC)

The Diversity-by-design project is funded by NCSC as one of the RITICS projects. Diversity-based approaches have been studied as an effective strategy to enhance the security and resilience of complex systems. The project aims to quantify the system diversity by identifying similarly vulnerable structures of components in interconnected systems. It mainly uses Graph Neural Networks (GNN) and other machine learning techniques to convert network graph data into vector representation and search for similarly vulnerable structures. We can then effectively evaluate human-input diversification strategies prior to actual deployment. The proposed work also provides an effective way to represent the CNI and other interconnected systems with the focus of identifying similarly vulnerable points of a system, which is able to provide insights into the resilience of the dependencies against replicated attacks and avoiding cascading failure.

A Framework for Risk-Informed Metrics-Enriched Cybersecurity Playbooks for CNI Resilience 

Investigator: Yulia Cherdantseva (PI), Tingting Li (Co-I) , Pete Burnap and Barney Craggs (Bristol)
Timeline: 2021-2023
Project value (funder): £503K (EPSRC EP/V038710/1)

The ultimate goal of the project is to improve CNI resilience in the UK by enabling timely and efficient incident response. To achieve this, this project will deliver a Framework for creating Risk-Informed Metrics-enriched Playbooks for Critical National Infrastructure (FRIMP4CNI). We propose to approach incident response playbooks in a fundamentally different way. First, playbooks in this project are integrated into core CNI processes affected by an incident, showing how enacting a particular response affects core processes as well as interdependent processes. Second, our playbooks address more than technical actions, they look at aspects beyond technology, e.g. operational response, issues related to staff availability and costs, reporting process, political and communication response. Third, playbooks are risk-informed because each playbook has an associated risk model; and fourth, they are enriched with business-driven multifaceted metrics which reflect the changes that an incident inflicts on a core process. The fifth feature is that our playbooks are optimal: an optimisation algorithm is applied to a set of alternative response strategies to identify the optimal response playbook for each case. A combination of the features listed above makes our approach unique and allows our playbooks to serve both as an action guide enabling improved cybersecurity incident response and as a decision support tool at the Board level.

Teaching

  • Module Lead, CM6125/CM6625 Database Systems, Spring Semester, 2019 - 2020, 2020-2021
  • Module Lead, CM6224/CM6724 Cyber Security, Autumn Semester, 2022 - present

Biography

  • Lecturer, Cardiff University, Nov. 2019 -
  • Postdoctoral Research Associate,  Imperial College London, 2014 - 2019
  • PhD in Artificial Intelligence, University of Bath
  • MSc in Computing, Imperial College London
  • BSc in Information Security, Xidian University, China

Supervisions

NEWS (02/2023): I have a funded PhD position on Cyber Resilience of Cyber-Physical Systems. Please see details here: Improving Cyber Resilience of Cyber-Physical Systems by Dynamic Diversification

In general, I am interested in supervising students in the areas of:

  • AI for Cyber Security
  • Cyber Defense against ICS/SCADA, CPS and Critical Infrastructure. 
  • Cybersecurity in Autonomous Systems
  • Adversarial Machine Learning for Cybersecurity

Current PhD Students

Iryna Bernyk (2021- )  on Cybersecurity Policies for Autonomous Vehicles

Sanyam Vyas (2021- ) on DRL and Automated Cyber Defence. 

Victoria Marcinkiewicz (2021- ) on Restore Trust in Autonomous Vehicles after Cyber Attacks. 

Stephen Morris (2023- )   

Sam Braithwaite (2024- )   

Current supervision

Sanyam Vyas

Sanyam Vyas

Research student

Stephen Morris

Stephen Morris

Research student

Iryna Bernyk

Iryna Bernyk

Graduate Demonstrator

Sam Braithwaite

Sam Braithwaite

Research student

Research themes

Specialisms

  • Cybersecurity
  • Artificial intelligence
  • Autonomous agents and multiagent systems