Hao Yan | Cyber security | Best Researcher Award

Published on by Computer Scientists Awards

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

🎓 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.

🏆 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.

🔬 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.

📌 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.

📚 Top Publications 

  1. Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
    🗓️ Published Year: 2023
    📘 Journal: Applied Sciences (ISSN: 2076-3417)
    📈 Cited by: 5 articles

  2. Graph-based Deep Learning for Intrusion Detection under Adversarial Environments
    🗓️ Published Year: 2023
    📘 Journal: IEEE Access
    📈 Cited by: 3 articles

  3. Edge-Level Graph Attention for Adversarial Robust Cyber Threat Identification
    🗓️ Published Year: 2024
    📘 Journal: Computers & Security
    📈 Cited by: 2 articles

  4. Joint Embedding and Edge Learning for Cyber Threat Modeling
    🗓️ Published Year: 2023
    📘 Journal: Soft Computing
    📈 Cited by: 1 article

  5. Adversarial Robustness in Cyber Intrusion Graph Learning
    🗓️ Published Year: 2024
    📘 Journal: ACM Transactions on Cyber-Physical Systems
    📈 Cited by: 1 article

 

Dr. Maher Alrahhal | Security | Best Researcher Award

Published on by Computer Scientists Awards

Dr. Maher Alrahhal | Security | Best Researcher Award

Postdoctoral, University of Sharjah, United Arab Emirates

Dr. Maher Abdul Moein Alrahhal is a Postdoctoral Research Associate at the Research Institute of Science and Engineering, University of Sharjah, UAE, and a Postdoctoral Fellow at Amity University Dubai, UAE. He holds a Ph.D. in Computer Science and Engineering from Jawaharlal Nehru Technological University (JNTU), Hyderabad, India, specializing in Artificial Intelligence, Big Data, and Data Analysis. With a solid background in computer science and engineering, Dr. Alrahhal has made significant contributions to the fields of machine learning, image retrieval, and data mining 🌐💡.

Publication Profile

Google Scholar

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🎓Education Background

Dr. Alrahhal’s educational journey is marked by excellence, with a Ph.D. in Computer Science and Engineering from JNTU, Hyderabad, India (March 2024). He completed his Master of Technology in Computer Science and Engineering with First Division from the National Institute of Technology, Warangal, India (July 2018). He holds a Bachelor’s degree in Computer Engineering from the University of Aleppo, Syria, graduating with honors and securing the first rank in his department 🏆📚.

👨‍🏫Professional Experience

Dr. Alrahhal has a robust academic career with over five years of teaching experience at prominent institutions in Syria and India. He has served as a Teaching Assistant at the University of Aleppo, and later as a Lecturer and Assistant Supervisor at JNTU, Hyderabad. Dr. Alrahhal also led the Big Data Lab at JNTU and played a key role in mentoring seven master’s students. His postdoctoral roles involve research and teaching at the University of Sharjah and Amity University Dubai, UAE 💻📖.

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🏅Awards and Honors

Dr. Alrahhal has received several prestigious awards, including the Best Paper Award at IEMTRONICS 2025 for his work on “Hybrid CNN for Efficient Content-Based Image Retrieval Cognitive Systems” 🥇. In recognition of his outstanding achievements, he was honored with the Alan Turing Award at the International Royal Golden Award ceremony (2023). Other notable accolades include the University Excellence Distinction for first-ranking in 2014 and multiple Al-Basel Certificates for Excellence 🏅🎖️.

🔍 Research Focus

Dr. Alrahhal’s research focuses on Artificial Intelligence, Machine Learning, Big Data, Data Mining, and Image Retrieval. His work explores the integration of deep learning techniques with image and video processing, multimedia systems, and the application of Hadoop for scalable data analysis. His contributions aim to advance content-based image retrieval systems and the development of intelligent systems for real-world applications 📊🤖.

💡🌐Conclusion

Dr. Maher Abdul Moein Alrahhal is a dynamic researcher and academic, committed to advancing the fields of Artificial Intelligence and Data Science. With numerous published works in high-impact journals and ongoing research initiatives, he continues to shape the future of intelligent systems and multimedia applications 🌟📈.

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📚Publications

Disruptive Attacks on Artificial Neural Networks: A Systematic Review of Attack Techniques, Detection Methods, and Protection Strategies, Intelligent Systems with Applications, in press.

MapReduce model for efficient image retrieval: a Hadoop-based framework, International Journal of Information Technology (Springer, Scopus Q1).

Enhancing Image Retrieval Systems: A Comprehensive Review of Machine Learning Integration In CBIR, International Journal of Intelligent Systems and Applications in Engineering, 12(4), 4195–4214.

Integrating Machine Learning Algorithms for Robust Content-Based Image Retrieval, International Journal of Information Technology, DOI: 10.1007/s41870-024-02169-2 (Springer, Scopus Q1).

Automatic diagnosis of epileptic seizures using entropy-based features and Multimodal Deep Learning Approaches, Medical Engineering and Physics, DOI: 10.1016/j.medengphy.2024.104206, (Elsevier, Scopus Q1).

Enhancing image retrieval accuracy through multi-resolution HSV-LNP feature fusion and modified K-NN relevance feedback, International Journal of Information Technology, DOI: 10.1007/s41870-024-02000-y, (Springer, Scopus Q1).