DR. MUHAMMAD FAIZ BIN MOHD ZAKI
Department of Computer System & Technology
Faculty of Computer Science and Information Technology
faizzakium.edu.myView CV | |
View 1-Page CV | |
Publons | |
Scopus Link | |
Biography | |
Faiz Zaki obtained his Master of Science (Web Science and Big Data Analytics) from the University College of London in 2017 and a PhD in Network Analytics from Universiti Malaya in 2022. He is currently serving as the Director of the Data and Information Management Center and a Senior Lecturer at the Department of Computer Systems and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya. He is also a core member of the Center of Research for Cybersecurity and Network (CSNET). His research interests lie at the intersection between big data analytics and computer networking. As such, most of his works revolve around network analytics, such as network traffic classification. Currently, his research direction is steering towards producing real-time network analytics using technologies like edge computing and federated learning. Faiz Zaki also holds several professional certifications in computer networking, such as CCNA and HCIA, besides being an active member of IEEE Computer Society and Young Professionals. |
Publication
Finance
Project Title | Progress | Status |
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A Cross-platform Information And Analytics System For Sales Of Property Transaction And Rental Information |
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on going |
A Lightweight Network Traffic Classifier For Resource-constrained Networks Using Explainable Artificial Intelligence |
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on going |
This information is generated from Research Grant Management System |
Machine learning for Internet of Things (IoT) device identification: a comparative study
Mobile Phone Network Data in the COVID-19 era: A systematic review of applications, socioeconomic factors affecting compliance to non-pharmaceutical interventions, privacy implications, and post-pandemic economic recovery strategies
Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks
Improved temporal IoT device identification using robust statistical features