DR. MUHAMMAD FAIZ BIN MOHD ZAKI
Department of Computer System & Technology
Faculty of Computer Science and Information Technology
faizzaki@um.edu.myView 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 a Senior Lecturer at the Department of Computer System and Technology,Faculty of Computer Science and Information Technology, Universiti Malaya. 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 Lightweight Network Traffic Classifier For Resource-constrained Networks Using Explainable Artificial Intelligence |
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new |
WP3: Time-Sensitive Sensor Integrated Food Rescue System Towards Combating Food Insecurity |
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on going |
A Cross-platform Information And Analytics System For Sales Of Property Transaction And Rental Information |
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on going |
This information is generated from Research Grant Management System |
The rise of website fingerprinting on Tor: Analysis on techniques and assumptions
GRAIN: Granular multi-label encrypted traffic classification using classifier chain
GRANULAR NETWORK TRAFFIC CLASSIFICATION FOR STREAMING TRAFFIC USING INCREMENTAL LEARNING AND CLASSIFIER CHAIN
Preliminary Study on the Effect of Traffic Representation on Accuracy Degradation in Machine Learning-based IoT Device Identification
Leveraging Federated Learning and XAI for Privacy-Aware and Lightweight Edge Training in Network Traffic Classification