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 Tingting Li

Tingting Li

Lecturer

School of Computer Science and Informatics

Email
lit29@cardiff.ac.uk
Campuses
Queen's Buildings, 5 The Parade, Newport Road, Cardiff, CF24 3AA
Users
Available for postgraduate supervision

Overview

NEWS (04/2021): I have a funded PhD position on Cybersecurity for Autonomous Vehicle. Please see details here: Decision Support System on Cybersecurity Policies for Autonomous Vehicles

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, 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 degree in Information Security (Xidian University, China).

Please visit her Personal Page for more details.

Biography

  • Lecturer, Cardiff University, 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

Publications

2020

2017

2016

2015

  • King, T. C.et al. 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

Teaching

  • Module Lead, CM6125/CM6625 Database Systems, Spring Semester, 2019 - 2020, 2020-2021

My research interests are Artificial Intelligence and its applications in Cyber Security. The research goals I have been pursuing are to develop AI techniques and adapt them to provide an intelligent defence to protect critical industrial control systems(ICS), cyber-physical systems and IoT systems from cyber attacks. In order to achieve that, I have applied traditional AI techniques to implement smart risk assessment and optimal network hardening, as well as using the latest deep learning techniques to realise fast and accurate intrusion detection and AI-enhanced stealthy attacks.

For the list of my publication, 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. 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.

Supervision

NEWS (04/2020): I have a funded PhD position for explainable AI and cyber security. Drop me an email if you are interested!

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

  • AI for Cyber Security
  • Explanable AI for Cyber Security
  • Cyber Security for Industrial Control Systems and other critical infrastructure.
  • Adversarial Machine Learning for Cyber Security
  • Multi-agent systems
  • Knowledge Representation and Reasoning