Navid Yazdanjue

University: University of Technology Sydney  

Academic supervisor: Amir H. Gandomi

Industry partner: Cyber Intelligence House (CIH)

Academic Background and Work Experience


Navid earned his Master of Science in Information Technology in 2018 from the Iran University of Science and Technology (IUST) in Tehran, Iran. He was a Research and Teaching Assistant with IUST from 2019 to 2022. At present, he is a Digital Finance Cooperative Research Centre (DFCRC) Industry Ph.D. candidate at the Data Science Institute of the University of Technology Sydney (UTS). He collaborates with Cyber Intelligence House (CIH) company as an industry partner within the aforementioned DFCRC Industry Ph.D. program. His research interests are primarily focused on meta-heuristic optimization techniques, evolutionary computation, machine learning models, social network analysis (SNA), and cybersecurity.


Thesis Topic


An Efficient Cyber Threat Detection Method Based on Enhanced Meta-heuristic and Classification Algorithms for a Real-world Cyber Dataset


About Cyber Intelligence House

Cyber Intelligence House is a leading cyber intelligence company specialised in helping cyber security professionals and law enforcement to assess and monitor cyber exposure from the dark web, deepweb, data breaches and online-assets. It is the trusted provider to government and law enforcement agencies globally, including Interpol and UNODC.

Cyber Intelligence House’s Cyber Exposure Platform (CEP) provides the world’s most comprehensive Cyber Threat database with over 10 years of data. 24/7 collection and storing of new data at a rate of ~600 pages per second. CEP delivers unrivalled search and alerting performance with Deep scanning of over 250 metadata factors and machine learning enabled categorization of threats to provide deep insights into potential cyber threats