Rate My Professor Masood Mansoori

Mansoori’s research specializations lie in intrusion detection and deception systems, particularly honeypots. He has designed and developed client honeypots YALIH and miniC, and contributed to open-source projects like Cowrie Honeypot through student supervision. Key publications include “Q-Cowrie: An adaptive honeypot to analyse attackers’ behaviour” (International Journal of Information Security, 2026, with M. Var Naseri, I. Welch, J. Haseeb); “Detection of advanced persistent threat: A genetic programming approach” (Applied Soft Computing, 2024, with A.A. Mamun, H. Al-Sahaf, I. Welch, S. Camtepe); “Behavior-based ransomware classification: A particle swarm optimization wrapper-based approach for feature selection” (Applied Soft Computing, 2022, with M.S. Abbasi, H. Al-Sahaf, I. Welch); “How do they find us? A study of geolocation tracking techniques of malicious web sites” (Computers and Security, 2020, with I. Welch); “Real-world IP and network tracking measurement study of malicious websites with HAZOP” (International Journal of Computers and Applications, 2017, with I. Welch, K.K.R. Choo, R.A. Maxion, S.E. Hashemi); and “Improving exposure of intrusion deception system through implementation of hybrid honeypot” (International Arab Journal of Information Technology, 2012, with O. Zakaria, A. Gani). He has received the UNSW Staff Recognition Enabler Award (2025), Teaching Excellence Nomination (2025), Best Student Paper Awards at ACSW-AISC (2013, 2014), Victoria University of Wellington Scholarship (2013), and CORE scholarship (2015).