Mr. Yixiang Zhang | Cybersecurity | Best Researcher Award

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Yixiang Zhang | Cybersecurity | Best Researcher Award

Yixiang Zhang, Huazhong University of Science and Technology, China

Zhang Yixiang 🎓 is a passionate researcher in cybersecurity, backend systems, and large language models (LLMs). A CPC member 🇨🇳 and top-ranking postgraduate student at Huazhong University of Science and Technology 🏫, he combines academic excellence with strong practical experience. He has led innovative R&D efforts in open-source algorithm evaluation, security assessments, and intelligent penetration testing 🤖. Zhang is skilled in Python, C++, LangChain, and vLLM, and has earned top national honors 🏆 for his contributions. With a curious mindset, strong adaptability, and a solid foundation in machine learning and security, he aims to solve complex challenges in cyberspace security 🔐.

Professional profile :

Orcid

Suitability for Best Researcher Award :

Zhang Yixiang exemplifies the qualities of a top-tier early-career researcher in the fields of cybersecurity,backend systems, and large language models (LLMs). As a top-ranking postgraduate student at Huazhong University of Science and Technology and a member of the Communist Party of China (CPC), he has demonstrated both academic excellence and a commitment to national scientific advancement. His profile reflects a strong blend of theoretical knowledge, technical innovation, and real-world impact, which are key attributes sought in a Best Researcher Award recipient.

Education & Experience :

🎓 Education:

  • 🏫 Huazhong University of Science and Technology (2023–2026)
    Master’s in Cyberspace Security | Top 25% | Advisor: Prof. Fu Cai
    🏅 First-Class Scholarship | 🏆 “Challenge Cup” National Winner

  • 🏫 Zhengzhou University (2019–2023)
    Bachelor’s in Information Security | Top 5%
    🏅 National Endeavor Scholarship | 🏅 First-Class Scholarship
    👨‍🎓 Outstanding Student & Youth League Cadre

💼 Experience:

  • 🧠 Open Source Algorithm Evaluation Engineer, Wuhan Jinyinhu Lab (2024–2025)
    🛠️ Platform Design | 📊 Document Optimization | 🧭 Strategic Planning

  • 💻 Backend Engineer, Institute of Software, Chinese Academy of Sciences (2024–2025)
    📐 Security Evaluation | 📄 Readability Modeling | 🧪 Standard Development

Professional Development :

Zhang Yixiang continues to evolve professionally through hands-on R&D projects in cybersecurity, backend infrastructure, and open-source intelligence 🧠. He has contributed to national-level platforms and collaborated with leading institutions like the Chinese Academy of Sciences 🏢. Proficient in LLM development frameworks like LangChain and vLLM, he actively refines models for risk detection, software component analysis, and AI-driven security auditing 🔍. His commitment to practical innovation is matched by academic rigor, with one patent filed and a top-tier journal paper under review 📄. Zhang thrives in fast-paced environments, always seeking to bridge cutting-edge tech with real-world security applications 🌐.

Research Focus :

Zhang Yixiang’s research centers around cyberspace security, LLM applications, and AI-driven algorithm optimization 🔐🤖. His projects include developing penetration testing frameworks, secure open-source evaluation platforms, and advanced detection algorithms for binary code analysis 🧬. He combines multi-agent systems and retrieval-augmented generation (RAG) architectures to improve automation and decision-making in security systems 🤝. His approach integrates deep learning methods, such as LSTM and PSO-optimized random forests, with practical applications like DDoS detection and open-source risk analysis 📊. Zhang’s interdisciplinary research bridges backend engineering, AI model fine-tuning, and cybersecurity intelligence to tackle complex, real-world digital threats 🚨.

Awards & Honors :

  • 🥇 First Prize, National “Challenge Cup” Innovation Competition (2024)

  • 🥇 First Prize, Challenge Cup – Special Project Division (2024)

  • 🥇 First Prize, 15th Provincial Computer Design Competition (2022)

  • 🥈 Second Prize, ICM/MCM U.S. Mathematical Modeling Competition (2021)

  • 🥉 Third Prize, APMCM Asia-Pacific Modeling Contest (2020)

  • 🏅 First-Class Academic Scholarship (2023, 2022)

  • 🏅 National Endeavor Scholarship (Zhengzhou University)

  • 🏅 Excellent Student Leader & Youth League Cadre

Publication Top Notes : 

Title: BinCoFer: Three-stage purification for effective C/C++ binary third-party library detection

Journal of Systems and Software, May 2025
DOI: 10.1016/j.jss.2025.112480
ISSN: 0164-1212

Citation (APA Style):
Zou, Y., Z., Y., Zhao, G., Wu, Y., Shen, S., & Fu, C. (2025). BinCoFer: Three-stage purification for effective C/C++ binary third-party library detection. Journal of Systems and Software, 112480. https://doi.org/10.1016/j.jss.2025.112480

Conclusion :

Zhang Yixiang stands out as a forward-looking, innovative researcher whose work aligns closely with the mission of the Best Researcher Award—to recognize exceptional contributions that advance scientific understanding and practical impact. His achievements in cybersecurity and LLM integration, combined with national recognition and hands-on leadership in cutting-edge projects, make him a compelling nominee. His trajectory suggests continued excellence and influential contributions to the field, justifying his selection for this prestigious honor.

Mr. Sangwon Lee | cybersecurity | Best Researcher Award

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Sangwon Lee | cybersecurity | Best Researcher Award

Sangwon Lee, Hoseo University, South Korea

Sangwon Lee is a passionate researcher in the field of cybersecurity 🔐 and artificial intelligence 🤖. She received her Bachelor’s degree in Computer Engineering from Hoseo University, South Korea 🇰🇷, in 2025. Currently, she is pursuing her Master’s in Information Security 🧠 at the same institution. Her research interests focus on AI security, physical security, and hardware-based security threats like clock glitch fault attacks ⏱️⚡. Sangwon is dedicated to advancing secure AI systems by identifying vulnerabilities and developing countermeasures. She is keen on blending academic insights with practical hardware testing to address real-world cybersecurity challenges.

Professional profile :

Orcid

Suitability for Best Researcher Award :

Sangwon Lee demonstrates exceptional promise as a young researcher by combining academic rigor with hands-on practical experimentation. Her deep focus on AI security and hardware-based threats, such as clock glitch fault attacks, highlights her commitment to tackling real-world vulnerabilities in next-generation computing systems. Her research embodies the spirit of innovation, curiosity, and relevance that aligns with the goals of the Best Researcher Award.

Education & Experience :

  • 📘 B.E. in Computer Engineering, Hoseo University, Republic of Korea (2025)

  • 🎓 M.S. in Information Security (ongoing), Hoseo University

  • 🔍 Researcher in AI & Hardware Security, focusing on fault injection and physical attack resistance

Professional Development :

Sangwon Lee is actively engaged in advanced studies in information security at Hoseo University 🏫. She continuously enhances her skills in cybersecurity 🧩 through hands-on research involving deep neural networks and fault attacks. As part of her academic journey, she explores real-world attack models such as clock glitching and implements robust countermeasures 🛡️. She regularly collaborates with fellow researchers and participates in seminars and workshops to stay updated on the latest developments in AI and hardware security 🔬. Her commitment to learning and innovation positions her as a promising figure in the cybersecurity and AI safety landscape 🌐.

Research Focus Area :

Sangwon Lee’s research is centered around the intersection of AI security 🤖 and hardware security 🛠️. Her primary focus involves studying vulnerabilities in deep neural networks exposed to physical fault injection techniques such as clock glitch attacks ⏱️⚡. She investigates how adversaries can exploit hardware-level weaknesses to manipulate AI system behavior and explores effective countermeasures. Her work aims to ensure robustness and trustworthiness in AI applications by integrating secure design principles and fault-resistant architectures 🔐. This cross-disciplinary approach connects machine learning with embedded system security, contributing significantly to the future of secure intelligent technologies 🔄🔍.

Awards and Honors :

  • 🎖️ Selected for Graduate Research Program in Information Security at Hoseo University

  • 🥇 Recognized for excellence in undergraduate thesis on AI & Security Integration

  • 📜 Commended for contribution to AI fault attack simulations in academic symposiums

Publication Top Notes : 

The publication you’re referring to is titled “Clock Glitch-based Fault Injection Attack on Deep Neural Network”, authored by Hyoju Kang, Seongwoo Hong, Youngju Lee, and Jeacheol Ha from Hoseo University. It was published in 2024 in the Journal of the Korea Institute of Information Security & Cryptology, Volume 34, Issue 5, pages 855–863. The paper investigates the impact of clock glitch-induced fault injections on deep neural networks (DNNs), particularly focusing on the forward propagation process and the softmax activation function. Using the MNIST dataset, the study demonstrates that injecting faults via clock glitches can lead to deterministic misclassifications, depending on system parameters. This research highlights the vulnerability of DNNs to hardware-level fault injections and underscores the need for robust countermeasures.

Citation:

Kang, H., Hong, S., Lee, Y., & Ha, J. (2024). Clock Glitch-based Fault Injection Attack on Deep Neural Network. Journal of the Korea Institute of Information Security & Cryptology, 34(5), 855–863. https://doi.org/10.13089/JKIISC.2024.34.5.855

Conclusion:

Sangwon Lee stands out as a proactive and visionary researcher whose work addresses the pressing security challenges in AI-driven technologies. Her commitment to building resilient, secure systems through both academic inquiry and practical experimentation makes her a highly deserving nominee for the Best Researcher Award.

Ms. Ujunwa Madububa Mbachu | Cybersecurity | Best Researcher Award

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Ms. Ujunwa Madububa Mbachu | Cybersecurity | Best Researcher Award

Ms. Ujunwa Madububa Mbachu, University of Southern Mississippi, United States

Ms. Ujunwa Madububa Mbachu is a Ph.D. candidate in Computer Science (Cybersecurity) at the University of Southern Mississippi, USA. She is a Visiting Instructor at the School of Computing Sciences and Computer Engineering and a Research Associate at the SPEN Lab, focusing on security and privacy in emerging networks. With extensive experience in teaching, research, and industry leadership, she is the President of the Cyberwatch Foundation, promoting inclusivity in cybersecurity education. Her expertise spans cybersecurity, machine learning, cloud computing, and privacy protection. She has received prestigious awards, including the Dissertation Completion Grant and Hall of Fame Induction at her university.

🌍 Professional Profile:

Google Scholar

🏆 Suitability for Best Researcher Award 

Ms. Mbachu is an exceptional candidate for the Best Researcher Award due to her groundbreaking contributions in cybersecurity, privacy, and emerging network security. Her Ph.D. research on Secure and Privacy-Aware Traffic Management Services in Autonomous Vehicles addresses critical global challenges in data protection and smart transportation. She has authored impactful research, led cybersecurity initiatives, and mentored students in computing sciences. As a leader in the Cyberwatch Foundation, she actively bridges academic research and real-world applications. Her dedication to advancing cybersecurity knowledge and fostering technological inclusivity makes her a highly deserving nominee for this prestigious recognition.

🎓 Education 

Ms. Mbachu is currently pursuing a Ph.D. in Computer Science (Cybersecurity) at the University of Southern Mississippi (2021–2025), with a dissertation on Secure and Privacy-Aware Traffic Management Services in Autonomous Vehicles, under the supervision of Dr. Ahmed Sherif. She earned an M.Sc. in Information Technology from the National Open University (NOUN), Nigeria (2017), focusing on the socioeconomic implications of national security and privacy systems. Her academic foundation includes a strong background in computer science, cybersecurity, and privacy research, preparing her for innovative contributions in data security, artificial intelligence, and cloud computing.

👩‍💻 Professional Experience

Ms. Mbachu is a Visiting Instructor at the University of Southern Mississippi, teaching computer science and IT courses across various modalities. She has also served as a Graduate Teaching Assistant, mentoring students and supporting research in cybersecurity. As a Research Associate at the SPEN Lab, she actively investigates security and privacy challenges in emerging networks. In the industry, she is the President of Cyberwatch Foundation, driving cybersecurity education initiatives. Her roles in academia and leadership demonstrate her commitment to advancing cybersecurity knowledge and empowering future researchers in the field.

🏅 Awards & Honors

Ms. Mbachu has received numerous accolades, including the 2025 Graduate School Dissertation Completion Grant and Hall of Fame Induction at the University of Southern Mississippi. She was also awarded the 2025 Student Travel Grant for her outstanding contributions to research. In 2021, she was honored with the College of Arts & Science Student Travel Award for her impactful academic work. These recognitions highlight her excellence in cybersecurity research, academic performance, and leadership in technology education. Her commitment to innovation and mentorship in cybersecurity has earned her prestigious acknowledgments from both academic and professional institutions.

🔬 Research Focus 

Ms. Mbachu’s research spans cybersecurity, privacy protection, machine learning, deep learning, and cloud computing. Her work focuses on securing emerging networks, with particular interest in privacy-aware traffic management in autonomous vehicles. She explores how artificial intelligence and cryptographic models enhance data security in smart infrastructures. Her studies also address cloud security, cyber-attack prevention, and AI-driven risk assessments. Through her leadership at the Cyberwatch Foundation, she advocates for inclusive cybersecurity education. Her multidisciplinary research contributes to both theoretical advancements and real-world cybersecurity applications, ensuring safer digital ecosystems in emerging technologies.

📖 Publication Top Notes 

  1. Machine Learning Techniques to Predict Mental Health Diagnoses: A Systematic Literature Review
    • Year: 2024
    • Citations: 7
  2. Predictive Machine Learning Approaches for Mental Health Diagnoses in College Students
    • Year: 2024
  3. A Review of Machine Learning Techniques to Predict Mental Health Diagnoses
    • Year: 2024
  1. Secure and Privacy-Preserving Aggregation Scheme for Traffic Management Systems
    • Year: 2023
    • Citations: 2
  2. Hardware-Acceleration Based Privacy-Aware Authentication Scheme for Internet of Vehicles
    • Year: 2024
  3. Privacy-Aware and Hardware Acceleration-Based Aggregation Scheme for Smart Grid Networks
    • Year: 2023