DR. MUHAMMAD FAIZ BIN MOHD ZAKI
Department of Computer System & Technology
Faculty of Computer Science and Information Technology
faizzakium.edu.my| View CV | |
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| Biography | |
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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. |
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| Project Title | Progress | Status |
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| A Large Language Model-Based Framework for Dynamic User Profiling in Learning Management Systems |
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new |
| BUILDING A CYBER-READY GENERATION OF STUDENTS |
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new |
| A Cross-platform Information And Analytics System For Sales Of Property Transaction And Rental Information |
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| This information is generated from Research Grant Management System | ||
Transformer-based tokenization for IoT traffic classification across diverse network environments
Mitigating adversarial attacks in federated learning based network traffic classification applications using secure hierarchical remote attestation and adaptive aggregation framework
Adversarial attack and defence of federated learning-based network traffic classification in edge computing environment
Machine learning for Internet of Things (IoT) device identification: a comparative study
