Senior Lecturer at Universiti Teknikal Malaysia Melaka, Malaysia
Ts. Dr. Warusia Mohamed Yassin is a Senior Lecturer (DS51) at Universiti Teknikal Malaysia Melaka (UTeM), specializing in Security in Computing. With a professional background in system programming, engineering, and cybersecurity analysis, she has contributed significantly to academia and industry in the fields of anomaly detection, intrusion prevention, and cyber risk management. She is a certified professional technologist (Ts.) and an active researcher with numerous national and international publications and research projects.
🔹Professional Profile:
Scopus Profile
Orcid Profile
Google Scholar Profile
🎓Education Background
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PhD in Computer Science (Security in Computing) – Universiti Putra Malaysia, 2015
Thesis: An Integrated Anomaly Intrusion Detection Scheme Using Statistical, Hybridized Classifier and Signature Approach.
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Master of Science in Computer Science (Security in Computing) – Universiti Putra Malaysia, 2011
Thesis: An Improved Hybrid Learning Approach For Better Anomaly Detection.
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Bachelor of Computer Science (Computer System) – Universiti Putra Malaysia, 2008
💼 Professional Development
She has taught various undergraduate and postgraduate courses including Computer Programming, Intrusion Detection and Prevention, Cyber Threat Intelligence, and Risk Management.
🔬Research Focus
Her main research interests lie in Security in Computing, particularly:
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Intrusion Detection Systems
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Malware Analysis
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Deep Learning and Machine Learning
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Deepfake Detection
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Biometric Authentication
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Cybersecurity Risk Management
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Blockchain-based Authentication
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IoT Security and Forensics
📈Author Metrics:
Dr. Warusia has co-authored numerous high-impact publications in journals and conference proceedings. Key contributions include works on hybrid learning methods, anomaly detection, K-Means clustering with Naïve Bayes, and genetic algorithm applications for cybersecurity. Some notable journals include Information Technology Journal, Journal of Information Assurance and Security, and CyberSec Conference Proceedings.
🏆Awards and Honors:
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Recipient of multiple industrial research grants including from CyberSecurity Malaysia, APNIC, and UTeM PJP.
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Recognized consultant for national-level cybersecurity projects, including railway infrastructure security.
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Active supervisor for PhD and Master students, with several completions under her mentorship.
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Holds the Professional Technologist (Ts.) title in Malaysia, signifying certified expertise in technology application and innovation.
📝Publication Top Notes
1. Ransomware Early Detection using Machine Learning Approach and Pre-Encryption Boundary Identification
Authors: W. Zanoramy, M.F. Abdollah, O. Abdollah, S.M.W.M. S.M.M
Journal: Journal of Advanced Research in Applied Sciences and Engineering Technology
Volume: 6
Year: 2025
DOI: [Not provided]
Abstract: Proposes a machine learning-based ransomware detection model that identifies pre-encryption behavior to enable proactive intervention and minimize system damage.
2. Routing Protocols Performance on 6LoWPAN IoT Networks
Authors: P.S. Chia, N.H. Kamis, S.F. Abdul Razak, S. Yogarayan, W. Yassin, et al.
Journal: IoT
Volume: 6(1), Page 12
Year: 2025
DOI: [Not provided]
Abstract: Compares multiple routing protocols in 6LoWPAN-based IoT environments to determine optimal performance in terms of energy efficiency, packet delivery, and delay.
3. An Enhanced Integrated Deep Learning Method to Overcome Dehazing Issues on Intelligent Vehicles
Authors: W.M. Yassin, A.I. Hajamydeen, M.F. Abdollah, K. Raja, N. Farzana
Book Title: Sustainable Smart Cities and the Future of Urban Development
Pages: 487–502
Year: 2025
DOI: [Not provided]
Abstract: Introduces an integrated deep learning framework to enhance dehazing in vision systems of intelligent vehicles, contributing to safer navigation in urban smart environments.
4. Optimizing Android Malware Detection Using Neural Networks and Feature Selection Method
Authors: J. Bintoro, F.A. Rafrastara, I.A. Latifah, W. Ghozi, W. Yassin
Journal: Jurnal Teknik Informatika (JUTIF)
Volume: 5(6), Pages 1663–1672
Year: 2024
DOI: [Not provided]
Abstract: Combines neural networks with a tailored feature selection method to boost the accuracy and efficiency of Android malware detection.
5. Enhancing XGBoost Performance in Malware Detection through Chi-Squared Feature Selection
Authors: S. Rosyada, F.A. Rafrastara, A. Ramadhani, W. Ghozi, W. Yassin
Journal: Jurnal Sisfokom (Sistem Informasi dan Komputer)
Volume: 13(3), Pages 396–402
Year: 2024
DOI: [Not provided]
Abstract: Employs Chi-squared feature selection to enhance XGBoost’s ability to detect malware, streamlining the model and improving classification results.
.Conclusion:
Dr. Warusia Mohamed Yassin is undoubtedly a deserving candidate for the Best Researcher Award. Her significant academic achievements, coupled with her active involvement in high-impact research and industry projects, demonstrate her contribution to advancing cybersecurity. Her innovative research in anomaly detection, malware analysis, and deep learning makes her a leader in the field, and her continued success in mentoring and supervising graduate students ensures that her expertise will benefit the next generation of cybersecurity professionals.