Jianbin Gao | Network Security | Best Researcher Award

Published on by Network Science

Dr. Jianbin Gao | Network Security | Best Researcher Award

University of Electronic Science and Technology of China | China

Author Profile

Scopus

Orcid ID

Google Scholar

EARLY ACADEMIC PURSUITS

Dr. Jianbin Gao began his academic journey in the field of Computer Science, culminating in a Ph.D. from the University of Electronic Science and Technology of China (UESTC) in 2012. His early research trajectory was further enriched through an international stint as a visiting scholar at the prestigious University of Pennsylvania, United States, from 2009 to 2011. This foundational experience played a significant role in shaping his global perspective and multidisciplinary research capabilities.

PROFESSIONAL ENDEAVORS

Since 2012, Dr. Gao has served as an Associate Professor in the School of Computer Science and Engineering (and the School of Cyber Security) at UESTC. His academic role encompasses teaching, supervising postgraduate research, and leading cutting-edge research initiatives in the domains of network security, AI security, and blockchain technologies. His institutional affiliation has been instrumental in cultivating strong research networks both within China and internationally.

CONTRIBUTIONS AND RESEARCH FOCUS ON NETWORK SECURITY

Dr. Gao's primary areas of expertise lie in network security, AI security, and blockchain, with a distinctive emphasis on applications in IoT, vehicular networks, and privacy-preserving systems. His recent work showcases groundbreaking innovations, including scalable and memory-efficient blockchain architectures, advanced graph embedding methods for fraud detection in complex networks, and smart contract verification techniques. His research uniquely bridges theoretical computer science with real-world implementation, particularly in domains such as secure vehicular communications and decentralized cloud services.

IMPACT AND INFLUENCE

Dr. Gao's research contributions have made a significant impact on the academic and professional communities, especially in cybersecurity and blockchain applications. His collaborative projects span multiple international teams and consistently address real-world problems like data integrity, privacy protection, and secure digital infrastructure. His work on secure Named Data Networking (NDN) and certificateless signcryption demonstrates a commitment to developing lightweight, practical, and scalable solutions for next-generation communication networks.

ACADEMIC CITES

With a total citation count of 2,956, an h-index of 19, and an i10-index of 28, Dr. Gao's scholarly output underscores both the depth and influence of his research contributions. These metrics reflect sustained academic recognition and the wide applicability of his findings across various domains of computer science and engineering.

LEGACY AND FUTURE CONTRIBUTIONS

Dr. Jianbin Gao stands at the forefront of cybersecurity and blockchain innovation, with a research portfolio that not only addresses today's challenges but also sets the stage for future technological advancements. His leadership in developing secure architectures for IoT, blockchain, and AI-driven systems signals a forward-thinking academic committed to real-world impact. As global digital infrastructures evolve, Dr. Gao’s work will likely remain pivotal in shaping secure, privacy-aware, and decentralized systems. His mentoring of young scholars and continued publication in top-tier journals ensure that his legacy will influence future generations of researchers and technologists.

SELECTED SCHOLARLY WORKS

  • A Blockchain-NDN Enabled Framework for Secure Vehicular Networking, IEEE Transactions on Networking (2025)

  • A Scalable and Memory-Efficient Architecture for Blockchain-Based IoT Privacy and Security, IEEE IoT Journal (2025)

  • Advanced Temporal Graph Embedding for Detecting Fraudulent Transactions, Book Chapter (2025)

  • MGGPT: A Multi-Graph GPT-enhanced Framework for Dynamic Fraud Detection, Computer Networks (2025)

  • Cloud-Service-Based Blockchain Infrastructure for ML Data Incentives, IEEE IoT Journal (2025)

  • HIBA: Hierarchical High-Performance Blockchain Architecture, IEEE Transactions on Networking (2025)

  • Precision-Guided Smart Contract Fuzzing by Static Analyses, Conference Paper (2025)

  • PRIDN: Privacy Preserving Data Sharing on Named Data Networking, IEEE TIFS (2024)

NOTABLE PUBLICATIONS

"A Blockchain-NDN Enabled Framework for Secure Vehicular Networking" 

  • Author: Christian Nii Aflah Cobblah; Qi Xia; Goodlet Akwasi Kusi; Isaac Amankona Obiri; Hu Xia; Jianbin Gao
  • Journal: IEEE Transactions on Networking
  • Year: 2025

"A Scalable and Memory-Efficient Architecture for Blockchain-Based IoT Privacy and Security" 

  • Author: Hu Xia; Christian Nii Aflah Cobblah; Qi Xia; Jianbin Gao
  • Journal: IEEE Internet of Things Journal
  • Year: 2025

"Advanced Temporal Graph Embedding for Detecting Fraudulent Transactions on Complex Blockchain Transactional Networks" 

  • Author: Jianbin Gao; Ansu Badjie; Qi Xia; Patrick Mukala; Hu Xia; Grace Mupoyi Ntuala
  • Journal: Book chapter
  • Year: 2025

"MGGPT: A Multi-Graph GPT-enhanced framework for dynamic fraud detection in cryptocurrency networks" 

  • Author: Ansu Badjie; Grace Mupoyi Ntuala; Qi Xia; Jianbin Gao; Hu Xia
  • Journal: Computer Networks
  • Year: 2025

"Cloud-Service-Based Blockchain Infrastructure for ML Data Incentives" 

  • Author: Goodlet Akwasi Kusi; Qi Xia; Jianbin Gao; Hu Xia; Christian N. A. Cobblah
  • Journal: IEEE Internet of Things Journal
  • Year: 2025

Gaber Al-Absi | Network Security | Best Researcher Award

Published on by Network Science

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.

Masoumeh Jafari | Network Security | Best Researcher Award

Published on by Network Science

Ms. Masoumeh Jafari | Network Security | Best Researcher Award

Visiting student, NUS University, Singapore📖

Masoumeh Jafari is a Ph.D. candidate in Software Engineering at Yazd University, currently advancing her research as a visiting scholar at the National University of Singapore. With a strong academic foundation and over a decade of experience, she has developed expertise in blockchain, cyber security, and artificial intelligence, focusing on practical applications for secure data exchange and decentralized systems. Masoumeh’s work includes contributions to peer-reviewed journals, conference presentations, and collaborations on cutting-edge projects in incident response and threat prevention. Recognized for her innovative approach and commitment to interdisciplinary research, she is both an accomplished academic and a dedicated educator in the fields of software engineering and information technology.

Profile

Google Scholar Profile

Education Background🎓

Masoumeh Jafari is a dedicated scholar currently pursuing her Ph.D. in Software Engineering at Yazd University, Iran, where she ranked 41st in the national entrance exam. Her research excellence has led her to a prestigious one-year visiting scholar position at the National University of Singapore (NUS), beginning in July 2024. She holds a Master’s degree in Software Engineering from Payame Noor University in Tehran, completed in 2014, and a Bachelor’s degree in Computer Science from Shahid Bahonar University of Kerman, where she graduated in the top 10% of her class. Her solid academic foundation in computer science and engineering has set the stage for her ongoing contributions to advanced research in blockchain, cybersecurity, and AI.

Professional Experience🌱

Masoumeh Jafari is an experienced researcher and lecturer in software engineering, actively involved in research and project development related to blockchain, cyber security, machine learning, and IoT. Since 2020, she has served as a research assistant to Dr. Adibnia at Yazd University and collaborated with the Academic and Policy Affairs (APA) Department at Yazd University on initiatives like incident response and threat prevention. Additionally, she has served as a reviewer for prestigious journals, including Information Fusion, Information Sciences, and Soft Computing. She has also judged conference submissions across diverse areas such as IoT, sustainable development, and computer science applications.

Masoumeh’s career spans over 14 years in academia and industry, including roles in game design, blockchain-based projects, and robotics education. Her technical skills encompass languages like Python, Solidity, and C++, with expertise in simulation tools (NS2, CloudSim), big data platforms (Hadoop, Spark), and blockchain environments (Ethereum, Remix).

Research Focus🔬

Masoumeh’s research interests lie in the fields of blockchain technology, cybersecurity, artificial intelligence (AI), and Internet of Things (IoT). She is particularly focused on the practical applications of blockchain in security, healthcare, and smart contracts, exploring new frameworks and solutions for secure, decentralized networks. Her recent projects include blockchain for secure data sharing in IoT systems and deep learning for data analytics.

Author Metrics 

Masoumeh Jafari has published extensively, contributing papers in reputable journals such as IEEE and regularly participating as a peer reviewer for scientific publications. Her works cover a wide range of topics, from cybersecurity frameworks and blockchain advancements to machine learning applications in data mining. Her active engagement in interdisciplinary research has earned her recognition within the academic community, establishing her as a forward-thinking scholar and a contributor to software engineering and information systems.

Selected Certifications and Recognitions

  • Blockchain & Smart Contract Development – Academy of CoinIran
  • Malware Analysis – Maher Center, Iran Information Technology Organization
  • Machine Learning and Image Processing (Python) – Yazd University
  • Multiple Academic Awards (Top 3 rankings, Yazd University, 2021-2024)

Publications Top Notes 📄

  1. Internet of Things in Eye Diseases: Introducing a New Smart Eyeglasses Designed for Probable Dangerous Pressure Changes in Human Eyes
    Authors: G. Prouski, M. Jafari, H. Zarrabi
    Conference: IEEE International Conference on Computer and Applications (ICCA)
    Year: 2017
    Pages: 364-368
    Summary: This paper explores the development of innovative smart eyeglasses embedded with IoT sensors to monitor intraocular pressure, aiming to detect and alert users to potentially dangerous pressure fluctuations that could lead to eye diseases such as glaucoma. This solution provides a real-time monitoring system to support early intervention and reduce risks associated with eye pressure changes.
    Citations: 13
  2. Considerations to Spoken Language Recognition for Text-to-Speech Applications
    Authors: M.S. Rafieee, S. Jafari, H.S. Ahmadi, M. Jafari
    Conference: 2011 UKSim 13th International Conference on Computer Modelling and Simulation
    Year: 2011
    Summary: This paper discusses the challenges and considerations in spoken language recognition systems used in text-to-speech applications. It evaluates the factors influencing accuracy and proposes methodologies for improving the reliability of language recognition in automated systems.
    Citations: 13
  3. Internet of Things in Eye Diseases Using Smart Glasses
    Author: M. Jafari
    Journal: International Journal of Engineering Education (IJEE)
    Year: 2017
    Pages: 1034-1042
    Summary: Building on IoT applications in healthcare, this paper presents the design and functionality of smart glasses aimed at diagnosing and managing eye diseases. The paper elaborates on sensor technology, data transmission, and potential benefits for patients with chronic eye conditions, contributing to personalized medical solutions and preventive care.
    Citations: 2
  4. A Novel Method for Extracting Blood Vessels in Digital Retinal Images
    Author: M. Jafari
    Journal: Soft Computing Journal
    Volume: 10, Issue 1
    Year: 2021
    Pages: 110-121
    Summary: This paper introduces a new algorithm for extracting blood vessels in retinal images, which is a crucial step for diagnosing various eye diseases. Utilizing soft computing techniques, the method enhances image segmentation and detection accuracy in digital retinal imaging, improving diagnosis and facilitating automated eye health monitoring.
    Citations: 1 (recently cited)
  5. Isolation of Vessels in Retinal Color Images
    Author: M. Jafari
    Journal: Soft Computing Journal
    Year: 2022
    Summary: This publication presents advanced techniques for isolating blood vessels in retinal color images, essential for retinal disease detection and analysis. The study leverages soft computing and image processing methods to improve the clarity and precision of vessel isolation in complex retinal imaging scenarios.

Conclusion

Masoumeh Jafari demonstrates an exceptional blend of technical expertise, interdisciplinary research acumen, and a commitment to impactful solutions in cybersecurity and IoT applications. Her research is highly innovative, grounded in solid technical skills, and driven by a commitment to advancing secure, decentralized systems and healthcare technology. Recognizing her achievements with the Research for Best Researcher Award would honor not only her scholarly contributions but also her vision for transformative technology in both digital and healthcare domains. Further development in cross-disciplinary applications and communication could enhance her impact, making her an even stronger candidate for future awards. Overall, her work aligns exceptionally well with the values of the Research for Best Researcher Award, marking her as a deserving candidate.