About

Dr Chao Chen is currently a Senior Lecturer in Cybersecurity at College of Science and Engineering. He received his PhD degree in Information Technology from Deakin University in 2017. From 2016 to 2018, he worked as a Data Scientist at Telstra to create customer value from huge and heterogeneous data sources using advanced analytics and big data techniques. He then worked at Swinburne Unversity of Technology as a Research Scientist from 2018 to 2020. He is conducting interdisciplinary research between cybersecurity and artificial intelligence (AI), such as AI for cybersecurity and security issues in AI models. He has published more than 20 research papers in refereed international journals and conferences, such as IEEE Transactions on Information Forensics and Security (TIFS), Privacy Enhancing Technologies Symposium (PETS) and ACM Asia Conference on Computer & Communications Security (ASIACCS). One of his papers was the featured article of that issue (IT Professional Mar.-Apr. 2016).

Teaching
  • CC5904: IoT Security and Cloud Computing (Level 5; ONL)
  • CP1402: Internet Fundamentals (Level 1; TSV)
  • CP2414: Network Security (Level 2; SIN & TSV)
  • CP3401: e-Strategic Management (Level 3; SIN)
  • CP5806: Data Information: Management, Security, Privacy and Ethics (Level 5; ONL)
Interests
Research
  • Security and Privacy in AI
  • Software Vulnerability Analysis
  • Network Traffic Analysis
  • Insider Threat Detection
  • IoT Security
Experience
  • 2020 to present - Senior Lecturer, James Cook University
  • 2018 to 2020 - Research Scientist, Swinburne University of Technology
  • 2016 to 2018 - Data Scientist, Telstra
Research Disciplines
Socio-Economic Objectives
Honours
Awards
  • 2013 - Vice Chancellor Price - Deakin University
Publications

These are the most recent publications associated with this author. To see a detailed profile of all publications stored at JCU, visit ResearchOnline@JCU. Hover over Altmetrics badges to see social impact.

Journal Articles
Conference Papers
Current Funding

Current and recent Research Funding to JCU is shown by funding source and project.

Australian Research Council - Linkage - Projects

Defending AI based FinTech Systems against Model Extraction Attacks

Indicative Funding
$90,000 over 3 years (administered by Swinburne University of Technology)
Summary
This project aims to develop new methods for defending artificial intelligence (AI) based FinTech systems from highly potent and insidious model extraction attacks whereby an adversary can steal the AI model from the system to cause intellectual property (IP) violation, business advantage disruption, and financial loss. This can be achieved by examining various attack models, creating active and utility-preserving defences, and inventing non-removable watermarks on AI models. The outcomes are new tools for securing AI-based FinTech systems before deployment and tools for IP violation forensics post-deployment. Such capabilities are beneficial by improving the security and safety of FinTech systems and other nationally critical AI systems.
Investigators
Sheng Wen, Yang Xiang and Chao Chen (Swinburne University of Technology and College of Science & Engineering)
Keywords
Fintech; Cybersecurity; Artificial Intelligence; Model Extraction Attacks
Supervision

Advisory Accreditation: I can be on your Advisory Panel as a Primary or Secondary Advisor.

These Higher Degree Research projects are either current or by students who have completed their studies within the past 5 years at JCU. Linked titles show theses available within ResearchOnline@JCU.

Current
  • Defending Deep Neural Network Systems from Malicious Data Poisoning Attacks (PhD , Primary Advisor)
  • Detecting and Preventing Privacy Leakage from Android Applications with Deep Graph Neural Networks (PhD , Primary Advisor)
  • ML-based Ransomware Protection (PhD , Primary Advisor)
Collaboration

The map shows research collaborations by institution from the past 7 years.
Note: Map points are indicative of the countries or states that institutions are associated with.

  • 5+ collaborations
  • 4 collaborations
  • 3 collaborations
  • 2 collaborations
  • 1 collaboration
  • Indicates the Tropics (Torrid Zone)

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