Anomaly detection in Cyber-Physical Systems
SOMAD Preprocess
This deep learning project tried to detect anomalies in real time. I collaborated with DASH lab members.
The given dataset is error data generated by deep learning model. Original dataset is SWaT (Secure Water Treatment) dataset and HAI (HIL-based Augmented ICS) security dataset.
Additionally, I made a simple R package and Python module in the linked repo (click the Code button under the title).
Papers I am involved in
Cho, Jinwoo, Shahroz Tariq, Sangyup Lee, Young Geun Kim, Jeong-Han Yun, Jonguk Kim, Hyoung Chun Kim, and Simon S. Woo. 2019. “Contextual Anomaly Detection by Correlated Probability Distributions Using Kullback-Leibler Divergence.” Anchorage, Alaska, USA.
Kim, Young Geun, Jeong-Han Yun, Siho Han, Hyoung Chun Kim, and Simon S. Woo. 2021. “Revitalizing Self-Organizing Map: Anomaly Detection Using Forecasting Error Patterns.” In ICT Systems Security and Privacy Protection, edited by Audun Jøsang, Lynn Futcher, and Janne Hagen, 382–97. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-78120-0_25.
Yun, Jeong-Han, Jonguk Kim, Won-Seok Hwang, Young Geun Kim, Simon S. Woo, and Byung-Gil Min. 2022. “Residual Size Is Not Enough for Anomaly Detection: Improving Detection Performance Using Residual Similarity in Multivariate Time Series.” In Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, 87–96. SAC ’22. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3477314.3506990.
Young Geun Kim
Ph.D. Candidate in Department of Statistics
Researching long-range dependent time series, Bayesian econometrics, and time series deep learning models.