Rate My Professor Nan Sun

Dr. Nan Sun is a lecturer in the School of Systems and Computing at UNSW Canberra, University of New South Wales. She received her PhD degree and Bachelor's degree (Honours) in Information Technology from Deakin University. Prior to her current role, Dr. Sun served as a Research Fellow in the Centre for Cyber Security Research and Innovation (CSRI) at Deakin University, where she worked on the Development of Australian Cyber Criteria Assessment (DACCA) project. Her research specializations encompass cybersecurity, with a focus on data-driven prediction, visualization, and discovery of incidents through data analytics and machine learning techniques. Dr. Sun pursues interdisciplinary studies at the intersection of cybersecurity and artificial intelligence, applying AI to bolster cybersecurity defenses. She also develops systems to enhance users' cybersecurity awareness education and cyber information retrieval. In her teaching portfolio, she delivers courses such as ZEIT 8030 Big Data and Decision Analytics for Security, ZEIT 3103 Digital Forensics, and ZEIT 3010 IT Project 2.

Dr. Sun has secured multiple competitive research grants, including as Lead Chief Investigator for 'Defence against Fake Cyber Threat Intelligence through Deep Learning' (UNSW AI Seed Funding, $30,000, 2022), 'Towards Understandable, Robust and Actionable Cybersecurity Information Retrieval' (UNSW Recruitment Research Proposal Grant, $30,000, 2023), and 'Real-time Data Analytics for Cybersecurity Threat Detection and Response' (IFCyber, $12,000, 2025). Additional funding includes UNSW Rapid Research Funding for 'Ethically Aligned AI Cyberbullying Detection Tools' ($23,110, 2023) and collaborations with CSIRO Data61. Her key publications include 'Cyber Threat Intelligence Mining for Proactive Cybersecurity Defense: A Survey and New Perspectives' (IEEE Communications Surveys and Tutorials, 2023), 'Cyber Information Retrieval Through Pragmatics Understanding and Visualization' (IEEE Transactions on Dependable and Secure Computing, 2023), 'Data-Driven Cybersecurity Incident Prediction: A Survey' (IEEE Communications Surveys and Tutorials, 2019), 'Defining Security Requirements With the Common Criteria: Applications, Adoptions, and Challenges' (IEEE Access, 2022), 'On the Development of a Protection Profile Module for Encryption Key Management Components' (IEEE Access, 2023), and 'Near real-time twitter spam detection with machine learning techniques' (International Journal of Computers and Applications, 2022). These contributions advance proactive cybersecurity strategies and AI integration in security practices.