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
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Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
🗓️ Published Year: 2023
📘 Journal: Applied Sciences (ISSN: 2076-3417)
📈 Cited by: 5 articles
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Graph-based Deep Learning for Intrusion Detection under Adversarial Environments
🗓️ Published Year: 2023
📘 Journal: IEEE Access
📈 Cited by: 3 articles
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Edge-Level Graph Attention for Adversarial Robust Cyber Threat Identification
🗓️ Published Year: 2024
📘 Journal: Computers & Security
📈 Cited by: 2 articles
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Joint Embedding and Edge Learning for Cyber Threat Modeling
🗓️ Published Year: 2023
📘 Journal: Soft Computing
📈 Cited by: 1 article
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Adversarial Robustness in Cyber Intrusion Graph Learning
🗓️ Published Year: 2024
📘 Journal: ACM Transactions on Cyber-Physical Systems
📈 Cited by: 1 article