Sang Seo Park
Ulsan National Institute of Science and Technology · Computer Science
About the Lab
Professor Sang Seo Park's research lab specializes in cybersecurity for space systems and advanced atmospheric modeling. The lab develops machine learning-based intrusion detection systems for spaceborne robotic systems using ROS2 and DDS/RTPS protocols, focusing on real-time anomaly detection to ensure mission integrity. Additionally, the lab advances numerical weather prediction by correcting systematic biases in all-sky infrared radiance data using phase-aware techniques, particularly leveraging cloud-top temperature to improve heavy precipitation forecasts. The interdisciplinary work bridges cyber-physical security and environmental modeling with a strong emphasis on real-time performance and data-driven accuracy.
Research Overview
Research Output Trend
Figures are computed from collected data and may differ slightly.
Selected Papers
3본 논문은 ROS2 기반 우주체계에서 발생 가능한 사이버물리적 보안 위협에 대응하기 위해 머신러닝 기반 이상 행위 탐지 시스템을 제안한다. ROS2 및 DDS/RTPS 기반 통신은 실시간 데이터 교환에 적합하지만, RTPS의 개방형 구조로 인해 명령 삽입, 세션 변조, 서비스 거부 공격에 취약하여 우주 임무의 무결성과 가용성을 저해할 수 있다. 이를 해결하기 위해 RTPS 통 신 메타데이터를 활용한 경량 침입탐지 모델을 설계하였으며, 공개 RTPS Attack Dataset에서 시간 간격, 시퀀스 차이, 직렬 화 데이터를 특징으로 추출하고 LLaMA 기반 토크나이저를 적용하였다. LightGBM, XGBoost, CatBoost, Random Forest 모 델을 비교한 결과, LightGBM이 높은 탐지 성능과 빠른 학습 속도를 보여 실시간 우주체계 보안 환경에 가장 적합함을 확인하였다.
Abstract This repository provides the Python implementation and sample datasets for the Phase-Aware Bias Correction method, as described in the manuscript: "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature" (submitted to Journal of Advances in Modeling Earth Systems (JAMES)). Key Methodology The provided code addresses systematic biases in all-sky infrared (IR) radiances within numerical weather prediction (NWP) sys
Abstract This repository provides the Python implementation and sample datasets for the Phase-Aware Bias Correction method, as described in the manuscript: "Improving Heavy Precipitation Forecasts via Phase-Aware Bias Correction of All-Sky Infrared Radiances Using Cloud-Top Temperature" (submitted to Journal of Advances in Modeling Earth Systems (JAMES)). Key Methodology The provided code addresses systematic biases in all-sky infrared (IR) radiances within numerical weather prediction (NWP) sys
Research Areas
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