E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System

Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS has increased, various anomaly detection stud...

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Autores principales:	Chanwoong Hwang, Taejin Lee
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Explainable anomaly detection ICS HAI dataset Bi-LSTM XAI SHAP Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/41a2bface6d74a9d9ca59336bef9f8ab
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id	oai:doaj.org-article:41a2bface6d74a9d9ca59336bef9f8ab
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spelling	oai:doaj.org-article:41a2bface6d74a9d9ca59336bef9f8ab2021-12-04T00:00:13ZE-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System2169-353610.1109/ACCESS.2021.3119573https://doaj.org/article/41a2bface6d74a9d9ca59336bef9f8ab2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9568906/https://doaj.org/toc/2169-3536Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS has increased, various anomaly detection studies are being conducted to detect abnormal situations in industrial processes. However, anomaly detection in ICS suffers from numerous false alarms. When false alarms occur, multiple sensors need to be checked, which is impractical. In this study, when an anomaly is detected, sensors displaying abnormal behavior are visually presented through XAI-based analysis to support quick practical actions and operations. Anomaly Detection has designed and applied better anomaly detection technology than the first prize at HAICon2020, an ICS security threat detection AI contest hosted by the National Security Research Institute last year, and explains the anomalies detected in its model. To the best of our knowledge, our work is at the forefront of explainable anomaly detection research in ICS. Therefore, it is expected to increase the utilization of anomaly detection technology in ICS.Chanwoong HwangTaejin LeeIEEEarticleExplainable anomaly detectionICSHAI datasetBi-LSTMXAISHAPElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 140470-140486 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Explainable anomaly detection ICS HAI dataset Bi-LSTM XAI SHAP Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Explainable anomaly detection ICS HAI dataset Bi-LSTM XAI SHAP Electrical engineering. Electronics. Nuclear engineering TK1-9971 Chanwoong Hwang Taejin Lee E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
description	Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet. These changes increase the likelihood of security vulnerabilities and accidents. As the risk of cyberattacks on ICS has increased, various anomaly detection studies are being conducted to detect abnormal situations in industrial processes. However, anomaly detection in ICS suffers from numerous false alarms. When false alarms occur, multiple sensors need to be checked, which is impractical. In this study, when an anomaly is detected, sensors displaying abnormal behavior are visually presented through XAI-based analysis to support quick practical actions and operations. Anomaly Detection has designed and applied better anomaly detection technology than the first prize at HAICon2020, an ICS security threat detection AI contest hosted by the National Security Research Institute last year, and explains the anomalies detected in its model. To the best of our knowledge, our work is at the forefront of explainable anomaly detection research in ICS. Therefore, it is expected to increase the utilization of anomaly detection technology in ICS.
format	article
author	Chanwoong Hwang Taejin Lee
author_facet	Chanwoong Hwang Taejin Lee
author_sort	Chanwoong Hwang
title	E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
title_short	E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
title_full	E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
title_fullStr	E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
title_full_unstemmed	E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System
title_sort	e-sfd: explainable sensor fault detection in the ics anomaly detection system
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/41a2bface6d74a9d9ca59336bef9f8ab
work_keys_str_mv	AT chanwoonghwang esfdexplainablesensorfaultdetectionintheicsanomalydetectionsystem AT taejinlee esfdexplainablesensorfaultdetectionintheicsanomalydetectionsystem
_version_	1718373026069741568

E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System

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