Hao Yan | Cyber security | Best Researcher Award

Mr. Hao Yan | Cyber security | Best Researcher Award

Mr. Hao Yan – Phd Candidate, Harbin Institute of Technology, Shenzhen and Peng Cheng Laboratory, China.

Hao Yan is a dedicated Ph.D. candidate at the Harbin Institute of Technology (Shenzhen), where he is also affiliated with the Peng Cheng Laboratory. With a strong foundation in computer science and a keen interest in cyberspace security, he has quickly established himself in the research community. His academic path reflects a passion for innovation, particularly in the fields of graph representation learning and network intrusion detection. Hao Yan continues to make meaningful contributions to adversarial learning methodologies and cyber attack defense strategies through innovative research and collaborative projects at national and institutional levels.

Publication Profile

ORCID

πŸŽ“ Education Background

Hao Yan began his academic journey with a Bachelor’s degree from Dalian Maritime University in 2019. He then pursued and earned his Master’s degree from Tianjin University in 2022. Currently, he is working towards his Ph.D. at the School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen). His education has been consistently aligned with his focus on computer science, cyber security, and advanced AI models. His academic background provides a solid technical and theoretical base for his ongoing research endeavors in cyberspace intelligence and adversarial learning.

🏒 Professional Experience

Currently, Hao Yan is actively engaged as a Ph.D. candidate and researcher at Harbin Institute of Technology (Shenzhen) and concurrently contributes to cutting-edge research at Peng Cheng Laboratory. He is involved in several prestigious research grants, including projects under the Shenzhen Science and Technology Program, Major Key Project of PCL, and the National Natural Science Foundation of China. He has worked on research solutions that integrate industry demands, such as advanced detection systems in network security. His practical research application reflects a seamless blend of academic theory and real-world cybersecurity challenges.

πŸ† Awards and Honors

While formal recognitions are under process, Hao Yan’s growing influence is demonstrated by his research’s acceptance in top databases like Scopus, Web of Science, and Ei Compendex. His work has been supported by competitive grants such as the Shenzhen Science and Technology Program and the National Natural Science Foundation of China, showing trust in his research potential. Furthermore, his pending China patent (202510907668.2) represents his commitment to innovation and technological contribution. His consistent academic performance and recognition through funded projects are clear indicators of his rising reputation in the cybersecurity research domain.

πŸ”¬ Research Focus

Hao Yan’s research expertise lies at the intersection of Graph Representation Learning, Adversarial Learning, Cybersecurity, and Network Intrusion Detection. His core innovation, the Adversarial Hierarchical-Aware Edge Attention Learning Method (AH-EAT), introduces robust edge feature representation under adversarial conditions for hierarchical detection tasks. He explores novel ways to counter advanced cyber threats and adversarial manipulation in intelligent systems. His research demonstrates how graph structures and learning models can be applied for efficient, secure, and scalable cyber defense systems, making a valuable impact on future-oriented cybersecurity frameworks.

πŸ“Œ Conclusion

In summary, Hao Yan is a promising young researcher whose work addresses key cybersecurity issues through intelligent algorithms and adversarial learning frameworks. With strong academic foundations, growing publication records, institutional support, and patent contributions, Hao has established a well-defined niche in network security and AI-based detection. His contributions are paving the way for more robust, intelligent, and secure cyberspace systems, and his research trajectory shows high potential for future academic and industry breakthroughs.

πŸ“š Top PublicationsΒ 

  1. Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
    πŸ—“οΈ Published Year: 2023
    πŸ“˜ Journal: Applied Sciences (ISSN: 2076-3417)
    πŸ“ˆ Cited by: 5 articles

  2. Graph-based Deep Learning for Intrusion Detection under Adversarial Environments
    πŸ—“οΈ Published Year: 2023
    πŸ“˜ Journal: IEEE Access
    πŸ“ˆ Cited by: 3 articles

  3. Edge-Level Graph Attention for Adversarial Robust Cyber Threat Identification
    πŸ—“οΈ Published Year: 2024
    πŸ“˜ Journal: Computers & Security
    πŸ“ˆ Cited by: 2 articles

  4. Joint Embedding and Edge Learning for Cyber Threat Modeling
    πŸ—“οΈ Published Year: 2023
    πŸ“˜ Journal: Soft Computing
    πŸ“ˆ Cited by: 1 article

  5. Adversarial Robustness in Cyber Intrusion Graph Learning
    πŸ—“οΈ Published Year: 2024
    πŸ“˜ Journal: ACM Transactions on Cyber-Physical Systems
    πŸ“ˆ Cited by: 1 article

 

Dr. Muhammad Asad Saleem | Information Security | Best Researcher Award

Dr. Muhammad Asad Saleem | Information Security | Best Researcher Award

Post Doctoral Researcher, University of Electronic Science and Technology of China

Dr. Muhammad Asad Saleem is a distinguished researcher and academic in cyberspace security, currently serving as a Postdoctoral Researcher at the University of Electronic Science and Technology of China πŸ‡¨πŸ‡³. With a strong background in computer science, he has contributed significantly to network security, cryptographic protocols, and authentication mechanisms πŸ”. His research has been recognized internationally, earning him multiple high-impact publications πŸ“š in IEEE Transactions and Q1 journals. Passionate about fostering cybersecurity innovations, Dr. Saleem is dedicated to enhancing vehicular networks, blockchain security, and IoT authentication πŸš—πŸ’‘.

Publication Profile

πŸŽ“ Education

Dr. Saleem holds a Ph.D. in Computer Science and Technology (2021–2024) from the University of Electronic Science and Technology of China πŸŽ–οΈ, where he received the Excellent Student Award for his outstanding academic performance (CGPA 3.9/4.0). His doctoral research focused on privacy-preserving authenticated key-establishment protocols for vehicular ad-hoc networks πŸš˜πŸ”‘. Prior to that, he completed an MS in Computer Science (2018–2020) from COMSATS University Islamabad πŸ‡΅πŸ‡°, where he achieved a perfect 4.0/4.0 CGPA and was recognized as the Overall Batch Topper πŸ†.

πŸ’Ό Experience

Dr. Saleem has a strong academic career, beginning as a Lab Engineer (2018–2021) at COMSATS University Islamabad, where he taught foundational courses like Programming, Database Systems, and Network Security πŸ’». He later served as a Visiting Lecturer (2020–2021) at the University of Sahiwal, teaching advanced computer science subjects. From 2021 to 2024, he was a Lecturer in Computer Science at the Higher Education Department of Punjab, where he trained future cybersecurity experts. His transition to a Postdoctoral Researcher in cyberspace security at UESTC, China marks his continued pursuit of cutting-edge research in cryptographic algorithms and vehicular security systems πŸŒπŸ”’.

πŸ… Awards and Honors

Dr. Saleem’s academic excellence and research contributions have earned him several prestigious awards πŸ†. He was honored with the Excellent Student Award during his Ph.D. studies πŸ“œ and was the Overall Batch Topper in his MS program πŸ₯‡. His high-impact publications in top-tier Q1 journals have further solidified his reputation as a leading cybersecurity researcher.

πŸ” Research Focus

Dr. Saleem’s research primarily revolves around network security, cryptographic authentication, and secure vehicular networks πŸš—πŸ”‘. His work focuses on designing lightweight and efficient security protocols for IoT, blockchain-based systems, and intelligent transportation networks πŸŒπŸ”. His contributions have advanced secure key establishment mechanisms, privacy-preserving authentication, and cybersecurity solutions for smart cities and industrial IoT πŸ’‘πŸ”’.

πŸ“ Conclusion

Dr. Muhammad Asad Saleem is a dynamic researcher and educator, making significant strides in cybersecurity and network authentication πŸ›‘οΈ. With a strong academic background, extensive research experience, and a passion for innovation, he continues to contribute to the evolving landscape of secure communication systems. His high-impact publications and academic excellence place him among the most promising researchers in the field of cyberspace security πŸš€.

πŸ“š PublicationsΒ 

A Provably Secure Lightweight Key Agreement Protocol for Wireless Body Area Networks in Healthcare Systems (2023) – IEEE Transactions on Industrial Informatics (DOI Link) – Cited by 50+ articles πŸ”¬.

Blockchain and PUF-Based Secure Key Establishment Protocol for Cross-Domain Digital Twins in IIoT Architecture (2023) – Journal of Advanced Research (DOI Link) – Cited by 40+ articles πŸ“‘.

Provably Secure Authentication Protocol for Mobile Clients in IoT Environment Using Puncturable Pseudorandom Function (2021) – IEEE Internet of Things Journal (DOI Link) – Cited by 60+ articles πŸ“².

Authenticated Key Management Protocol in Fog Computing-Based Internet of Vehicles Deployment (2020) – IEEE Internet of Things Journal (DOI Link) – Cited by 55+ articles 🚘.

An Efficient and Physically Secure Privacy-Preserving Key-Agreement Protocol for Vehicular Ad-hoc Networks (2023) – IEEE Transactions on Intelligent Transportation Systems (DOI Link) – Cited by 35+ articles πŸš—.

A Provably Secure Mobile User Authentication Scheme for Big Data Collection in IoT-Enabled Maritime Intelligent Transportation System (2023) – IEEE Transactions on Intelligent Transportation Systems (DOI Link) – Cited by 45+ articles βš“.

Secure RFID-Assisted Authentication Protocol for Vehicular Cloud Computing Environment (2024) – IEEE Transactions on Intelligent Transportation Systems (DOI Link) – Cited by 30+ articles ☁️🚘.

A Cost-Efficient Anonymous Authenticated and Key Agreement Scheme for V2I-Based Vehicular Ad-hoc Networks (2024) – IEEE Transactions on Intelligent Transportation Systems (DOI Link) – Cited by 25+ articles 🏎️.

Lightweight and Secure Multi-Factor Authentication Scheme in VANETs (2023) – IEEE Transactions on Vehicular Technology (DOI Link) – Cited by 40+ articles 🏁.

Cloud-Assisted Secure and Cost-Effective Authenticated Solution for Remote Wearable Health Monitoring System (2023) – IEEE Transactions on Network Science and Engineering (DOI Link) – Cited by 50+ articles βŒšπŸ”’.