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 :
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📘 B.E. in Computer Engineering, Hoseo University, Republic of Korea (2025)
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🎓 M.S. in Information Security (ongoing), Hoseo University
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🔍 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 :
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🎖️ Selected for Graduate Research Program in Information Security at Hoseo University
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🥇 Recognized for excellence in undergraduate thesis on AI & Security Integration
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📜 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.