Research Team Introduction

Introducing the research members of Yonsei Universityโ€™s School of Software
who participated in developing the Side Channel Attack Detection and Defense System

School of Software, Yonsei University | RAISE LAB
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Sun Jun Hwang

Team Leader / Main Developer / Report Author

Led the overall project and was responsible for designing and implementing deep learning models. Played a key role in comparing the performance of MLP, CNN1D, and LSTM models and deriving experimental results. As a member of RAISE LAB, he has a deep interest in cryptography and security.

sunjun7559012@yonsei.ac.kr
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Gun Hyung Yoo

Presenter

Responsible for delivering research results clearly and effectively. Successfully communicated the projectโ€™s value and outcomes by explaining complex deep learning concepts and side channel attack theories in an understandable way.

conneryoo@yonsei.ac.kr
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Kwan Ho Kim

Data Collection and Preprocessing

Responsible for generating and preprocessing synthetic data. Contributed by building a dataset of 200,000 samples using make_classification and visualizing data distribution through PCA analysis.

salmonhouse12@yonsei.ac.kr
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Sung Ho Oh

Data Collection and Preprocessing

Managed data quality and optimized preprocessing for the experiments. Played a key role in data normalization and feature engineering, contributing to improved model training efficiency.

akdkdjfj@yonsei.ac.kr
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Ji Hyuk Ha

Presentation Design

Designed the projectโ€™s visual representation and created presentations to effectively deliver research results. Translated complex technical content into intuitive graphics and charts to improve understanding.

jihyeok0502@yonsei.ac.kr

Research Collaboration

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School of Software, Yonsei University

Systematic learning and research activities based on advanced educational environment and excellent research infrastructure.

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RAISE LAB

In-depth research and professional guidance in a laboratory specialized in cryptography and security.

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Cryptography Course

Acquisition of theoretical foundations and development of practical application skills through the Spring 2025 Cryptography course.

Team Achievements

5
Members
3
Deep Learning Models
99.4%
Best Accuracy
200K
Training Data

Team Values

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Innovation

Presenting a new approach using deep learning in the field of side channel attack detection.

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Collaboration

Team members with diverse expertise fulfill their roles and create synergy.

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Growth

Continuously developing individual and team expertise through ongoing learning and research.

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Security

Recognizing the importance of cybersecurity and contributing to building a safe digital environment.