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.
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:
- Gaber A. Al-Absi: Lead author; developed the STC-GraphFormer model and conducted extensive experiments.
- Yong Fang: Co-author; contributed to the design and evaluation of the intrusion detection framework.
- 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)
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.