- PhD student
I am a PhD candidate in Computer Science studying under the supervision of Professor Alice Hutchings. My research interests include computer security, cybercrime, underground markets and data collection from various online sources.
Biography
I completed my BSc in Computer Science from Vrije Universiteit Amsterdam and MSc in Computer Science (Computer Systems Security track) from Vrije Universiteit and University of Amsterdam, under the supervision of Professor Fabio Massacci. My final Master's project explored the use of machine learning for vulnerability detection in source code. Before starting my PhD, I was a research assistant at the Cambridge Cybercrime Centre where I was responsible for maintenance and expansion of cybercrime related datasets containing criminal and extremist content collected from a number of forums, telegram and discord channels. Before that I worked as a research assistant at Network Institute of Vrije Universiteit Amsterdam researching technical and legal requirements under the GDPR.
Themes
Teaching
I have supervised or currently supervise the following courses:
- Cybersecurity
- Operating Systems
- Databases
Publications
- Tina Marjanov, Alice Hutchings. SoK: Digging into the Digital Underworld of Stolen Data Markets. IEEE Symposium on Security and Privacy (SP), 2025
- Jonah Gibbon, Tina Marjanov, Alice Hutchings, John Aston. Measuring the Unmeasurable: Estimating True Population of Hidden Online Communities. IEEE European Symposium on Security and Privacy Workshops (EuroS&PW), 2024
- Tina Marjanov, Konstantinos Ioannidis, Tom Hyndman, Nicolas Seyedzadeh, Alice Hutchings. Breaking the Ice: Using Transparency to Overcome the Cold Start Problem in an Underground Market. 23rd Workshop on the Economics of Information Security (WEIS), 2024
- Tina Marjanov, Maria Konstantinou, Magdalena Jóźwiak, Dayana Spagnuelo. Data Security on the Ground: Investigating Technical and Legal Requirements under the GDPR. Proceedings on Privacy Enhancing Technologies (PETs), 2023
- Tina Marjanov, Ivan Pashchenko, Fabio Massacci. Machine Learning for Source Code Vulnerability Detection: What Works and What Isn't There Yet. IEEE Security & Privacy, 2022