Warusia Yassin | Cybersecurity | Best Researcher Award

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Dr. Warusia Yassin | Cybersecurity | Best Researcher Award

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

  • 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.

  • Master of Science in Computer Science (Security in Computing) – Universiti Putra Malaysia, 2011
    Thesis: An Improved Hybrid Learning Approach For Better Anomaly Detection.

  • Bachelor of Computer Science (Computer System) – Universiti Putra Malaysia, 2008

💼 Professional Development

  • Senior Lecturer, Universiti Teknikal Malaysia Melaka (2015 – Present)

  • Security Analyst, 2009

  • System Engineer, 2007

  • Lab Demonstrator, 2007

  • System Programmer, 2004

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:

  • Intrusion Detection Systems

  • Malware Analysis

  • Deep Learning and Machine Learning

  • Deepfake Detection

  • Biometric Authentication

  • Cybersecurity Risk Management

  • Blockchain-based Authentication

  • 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:

  • Recipient of multiple industrial research grants including from CyberSecurity Malaysia, APNIC, and UTeM PJP.

  • Recognized consultant for national-level cybersecurity projects, including railway infrastructure security.

  • Active supervisor for PhD and Master students, with several completions under her mentorship.

  • 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.

Gaber Al-Absi | Network Security | Best Researcher Award

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Mr. Gaber Al-Absi | Network Security | Best Researcher Award

Gaber Al-Absi, at Chang’an University, Yemen📖

Gaber Ahmed Al-Absi is currently a Ph.D. candidate in Information Engineering at Chang’an University, Xi’an, China, with a focus on advanced technologies in blockchain and network systems. He holds a Master’s in Software Engineering from Northeastern University, China, where he was recognized for outstanding academic performance. Gaber is a highly skilled IT technician, network engineer, and educator, with extensive experience in both academic and professional settings. His research interests include blockchain technology, decentralized storage systems (IPFS), and machine learning applications.

Profile

Orcid Profile

Education Background🎓

Gaber Ahmed Al-Absi is currently pursuing a Ph.D. in Information Engineering at Chang’an University in Xi’an, China, a program he began in December 2022. He completed his Master’s in Software Engineering at Northeastern University in Shenyang, China, in 2022, where he was recognized for his outstanding academic performance with a GPA of 3.7. During his master’s studies, he received an award for his exceptional performance in the 2019-2020 academic year. Al-Absi’s academic credentials are complemented by several professional training certifications, including those in machine learning, blockchain technology, and smart learning, which were earned through various online platforms such as Deeplearning.AI, Coursera, and Udemy. Additionally, he holds a Bachelor’s degree in Computer Network Engineering Technology from Sana’a Community College in Yemen, where he graduated with a first-rank distinction. Al-Absi also holds certifications in Cisco Networking and computer maintenance, further enhancing his technical expertise.

Professional Experience🌱

Gaber has a diverse professional background, having worked in various roles, including as an IT technician, network engineer, and academic specialist. He has been involved in numerous network infrastructure projects, including configuring firewalls, switches, and routers for various companies. Gaber has also contributed to network design, security analysis, and system maintenance for firms such as ITEX Solutions, Griffin-LTD Group, and Al-Nasser University. He has held teaching assistant roles at several universities in Yemen, instructing students in computer networks and IT-related courses. Additionally, Gaber has completed multiple internships and industrial training in network management and VoIP systems at prominent institutions in Yemen

Research Interests🔬

1.Gaber’s research interests focus on:

  • Blockchain technology, particularly Hyperledger Fabric.
  • Decentralized storage solutions, such as the Interplanetary File System (IPFS).
  • Machine learning and computer vision applications.
  • Network security and IT infrastructure optimization.

Author Metrics

Gaber has contributed to multiple academic and professional projects, including designing secure electronic health record systems using blockchain and voice-over-IP network systems. His work on network security, blockchain applications, and decentralized storage systems reflects a strong academic and practical foundation in information technology and network engineering.

Skills & Certifications

  • Networking & IT Infrastructure: Cisco CCNA, Juniper Networks, ITIL, Hyperledger Fabric, IPFS.
  • Programming & Technologies: Python, Java, Angular, Machine Learning, Blockchain.
  • Certifications: Various online certifications in Machine Learning, Blockchain, Cloud Security, Deep Learning, and IT management (Deeplearning.AI, Coursera, SkillUP, Udemy).
  • Languages: Arabic (Native), English (Fluent), Chinese (HSK Level 3).

Interests & Activities

Gaber is passionate about emerging technologies, particularly in blockchain and decentralized systems. He enjoys reading about technological innovations, traveling, and exploring new opportunities in research and development.

Publications Top Notes 📄

1. STC-GraphFormer: Graph Spatial-Temporal Correlation Transformer for In-vehicle Network Intrusion Detection System

  • Authors: Gaber A. Al-Absi, Yong Fang, Adnan A. Qaseem
  • Journal: Vehicular Communications
  • Publication Date: Available online 5 December 2024
  • DOI: Link to Paper
  • Abstract: The paper proposes a novel method called STC-GraphFormer, which is a Graph Spatial-Temporal Correlation Transformer for detecting intrusions in in-vehicle networks. This approach leverages graph-based modeling and transformers to address the challenges of detecting complex, dynamic intrusions within vehicular network environments. By incorporating spatial and temporal correlations between nodes in the vehicle’s network, the system aims to enhance the accuracy and efficiency of intrusion detection in real-time applications. The paper discusses the design, implementation, and evaluation of the model, showing improvements over traditional methods in terms of detection rate and false alarm reduction.
  • Keywords: In-vehicle Network, Intrusion Detection System, Spatial-Temporal Correlation, Graph Neural Networks, Transformer, Vehicular Communications, Cybersecurity
  • Contributions:
  1. Gaber A. Al-Absi: Lead author; developed the STC-GraphFormer model and conducted extensive experiments.
  2. Yong Fang: Co-author; contributed to the design and evaluation of the intrusion detection framework.
  3. Adnan A. Qaseem: Co-author; provided theoretical insights and supported the implementation of the system.
  • Funding and Acknowledgments:
    (Details may include funding sources and acknowledgments of any supporting institutions or research facilities, which are not available in the provided information)

Conclusion

Gaber A. Al-Absi is a highly promising researcher with a solid foundation in network engineering, blockchain, and machine learning. His recent contributions to the field of network security, particularly through the development of innovative methods like the STC-GraphFormer, have the potential to make significant advancements in vehicular network systems and cybersecurity. While there is room for improvement in broadening his collaborations and expanding his publication record, his technical expertise, academic achievements, and commitment to research make him an ideal candidate for the Best Researcher Award.