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Research on Data Tuning Methods to Improve the Anomaly Detection Performance of Industrial Control Systems
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산업제어시스템의 이상 탐지 성능 개선을 위한 데이터 보정 방안 연구

논문 기본 정보

Type
Academic journal
Author
SANGSO JUN (고려대학교) Kyung-ho Lee (고려대학교)
Journal
Korea Institute Of Information Security And Cryptology Journal of the Korea Institute of Information Security & Cryptology Vol.32 No.4 KCI Accredited Journals
Published
2022.8
Pages
691 - 708 (18page)

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Research on Data Tuning Methods to Improve the Anomaly Detection Performance of Industrial Control Systems
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Research history (2)

2023

산업제어시스템의 이상 탐지 성능 개선을 위한 데이터 보정 방안 연구
전상수 정보보호학과 2023.01 Thesis

2022

Research on Data Tuning Methods to Improve the Anomaly Detection Performance of Industrial Control Systems
SANGSO JUN ,  Kyung-ho Lee Journal of the Korea Institute of Information Security & Cryptology 2022.08 Academic journal
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Abstract· Keywords

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As the technology of machine learning and deep learning became common, it began to be applied to research on anomaly(abnormal) detection of industrial control systems. In Korea, the HAI dataset was developed and published to activate artificial intelligence research for abnormal detection of industrial control systems, and an AI contest for detecting industrial control system security threats is being conducted. Most of the anomaly detection studies have been to create a learning model with improved performance through the ensemble model method, which is applied either by modifying the existing deep learning algorithm or by applying it together with other algorithms. In this study, a study was conducted to improve the performance of anomaly detection with a post-processing method that detects abnormal data and corrects the labeling results, rather than the learning algorithm and data pre-processing process. Results It was confirmed that the results were improved by about 10% or more compared to the anomaly detection performance of the existing model.
#Anomaly Detection #ICS Securiy #Time Sereies Data #HAI Dataset #SWaT

Contents

요약
ABSTRACT
I. 서론
II. 관련 연구
III. 데이터 후처리 보정을 통한 성능 개선
IV. 성능 개선 검증
V. 결론
References

References (26)

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Shalyga, Dmitry, Pavel Filonov, and Andrey Lavrentyev. "Anomaly detection for water treatment system based on neural network with automatic architecture optimization." arXiv preprint arXiv:1807.07282 (2018). Xu, Jiehui, et al. "Anomaly transformer: Time series anomaly detection with association discrepancy." arXiv preprint arXiv:2110.02642 (2021). Blázquez-García, Ane, et al. "A review on outlier/anomaly detection in time series data." ACM Computing Surveys (CSUR) 54.3 (2021): 1-33. google schola Braei, Mohammad, and Sebastian Wagner. "Anomaly detection in univariate time-series: A survey on the state-of-the-art." arXiv preprint arXiv:2004.00433 (2020). Filonov, Pavel, Andrey Lavrentyev, and Artem Vorontsov. "Multivariate industrial time series with cyber-attack simulation: Fault detection using an lstm-based predictive data model." arXiv preprint arXiv:1612. 06676 (2016).

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