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Two ICS Security Datasets and Anomaly Detection Contest on the HIL-based Augmented ICS Testbed

Authors:

Hyeok-Ki Shin,

Woomyo Lee,

Jeong-Han Yun,

Byung-Gil MinAuthors Info & Claims

CSET '21: Proceedings of the 14th Cyber Security Experimentation and Test Workshop

Pages 36 - 40

https://doi.org/10.1145/3474718.3474719

Published: 07 September 2021 Publication History

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Abstract

Security datasets with various operating characteristics and abnormal situations of industrial control system (ICS) are essential to develop artificial intelligence (AI)-based control system security technology. In this study, we built a hardware-in-the-loop (HIL)-based augmented ICS (HAI) testbed and developed ICS security datasets. Here, we introduce the second dataset (HAI 21.03), which was developed with the user feedback of the first released version (HAI 20.07). All HAI datasets are publicly available at https://github.com/icsdataset/hai. HAI 21.03 was expanded by adding data points and normal/attack scenarios to HAI 20.07. We also held an AI-based anomaly detection contest (HAICon 2020) utilizing the HAI datasets developed so far, giving many AI researchers an opportunity to discuss and share ideas for ICS anomaly detection research. This paper presents the results of the HAICon 2020. The results of the top teams in the competition can be used as a performance comparison criterion when using HAI 21.03.

References

[1]

Won-Seok Hwang, Jeong-Han Yun, Jonguk Kim, and Hyoung Chun Kim. 2019. Time-Series Aware Precision and Recall for Anomaly Detection: Considering Variety of Detection Result and Addressing Ambiguous Labeling. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management. ACM, 2241–2244.

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[2]

Siddhartha Kumar Khaitan and James D McCalley. 2014. Design techniques and applications of cyberphysical systems: A survey. In IEEE Systems Journal.

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[3]

Hyeok-Ki Shin, Woomyo Lee, Jeong-Han Yun, and HyoungChun Kim. 2019. Implementation of Programmable CPS Testbed for Anomaly Detection. In 12th USENIX Workshop on Cyber Security Experimentation and Test(CSET 19).

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[4]

H.-K. Shin, W. Lee, J.-H. Yun, and H. C. Kim. 2020. HAI 1.0: HIL-based Augmented ICS Security Dataset. In 13th USENIX Workshop on Cyber Security Experimentation and Test(CSET 20).

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Song HYuan YWang YYang JLuo HLi S(2024)A Security Posture Assessment of Industrial Control Systems Based on Evidential Reasoning and Belief Rule BaseSensors10.3390/s2422713524:22(7135)Online publication date: 6-Nov-2024
https://doi.org/10.3390/s24227135
Tanyıldız HBatur Şahin CBatur Dinler Ö(2024)Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control SystemsApplied Sciences10.3390/app1420928714:20(9287)Online publication date: 12-Oct-2024
https://doi.org/10.3390/app14209287
Li LQu HLi ZZheng JTang XLiu P(2024)Data Reconstruction via Consensus Graph Learning for Effective Anomaly Detection in Industrial IoTIEEE Transactions on Industrial Informatics10.1109/TII.2023.331622020:3(3996-4006)Online publication date: Mar-2024
https://doi.org/10.1109/TII.2023.3316220
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Two ICS Security Datasets and Anomaly Detection Contest on the HIL-based Augmented ICS Testbed
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CSET '21: Proceedings of the 14th Cyber Security Experimentation and Test Workshop

August 2021

95 pages

ISBN:9781450390651

DOI:10.1145/3474718

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 07 September 2021

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CSET '21: Cyber Security Experimentation and Test Workshop

August 9, 2021

CA, Virtual, USA

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Song HYuan YWang YYang JLuo HLi S(2024)A Security Posture Assessment of Industrial Control Systems Based on Evidential Reasoning and Belief Rule BaseSensors10.3390/s2422713524:22(7135)Online publication date: 6-Nov-2024
https://doi.org/10.3390/s24227135
Tanyıldız HBatur Şahin CBatur Dinler Ö(2024)Improving Deceptive Patch Solutions Using Novel Deep Learning-Based Time Analysis Model for Industrial Control SystemsApplied Sciences10.3390/app1420928714:20(9287)Online publication date: 12-Oct-2024
https://doi.org/10.3390/app14209287
Li LQu HLi ZZheng JTang XLiu P(2024)Data Reconstruction via Consensus Graph Learning for Effective Anomaly Detection in Industrial IoTIEEE Transactions on Industrial Informatics10.1109/TII.2023.331622020:3(3996-4006)Online publication date: Mar-2024
https://doi.org/10.1109/TII.2023.3316220
Xue YPan JGeng YYang ZLiu MDeng R(2024)Real-Time Intrusion Detection Based on Decision Fusion in Industrial Control SystemsIEEE Transactions on Industrial Cyber-Physical Systems10.1109/TICPS.2024.34065052(143-153)Online publication date: 2024
https://doi.org/10.1109/TICPS.2024.3406505
Kim BAlawami MKim EOh SPark JKim H(2023)A Comparative Study of Time Series Anomaly Detection Models for Industrial Control SystemsSensors10.3390/s2303131023:3(1310)Online publication date: 23-Jan-2023
https://doi.org/10.3390/s23031310
Levshun DKotenko I(2023)Application of Intelligent Methods of Correlation of System Events in Predictive Analysis of Security States of Objects of Critical InfrastructurePattern Recognition and Image Analysis10.1134/S105466182303026433:3(389-397)Online publication date: 1-Sep-2023
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Deng LZhang XZheng QZhao DZhou JXiang J(2023)A New Dataset for Intrusion Detection in Industrial Control System: A Gas Pipeline Testbed Study2023 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom)10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00149(887-892)Online publication date: 21-Dec-2023
https://doi.org/10.1109/ISPA-BDCloud-SocialCom-SustainCom59178.2023.00149
Park JKim BKim H(2023)MENDEL: Time series anomaly detection using transfer learning for industrial control systems2023 IEEE International Conference on Big Data and Smart Computing (BigComp)10.1109/BigComp57234.2023.00049(261-268)Online publication date: Feb-2023
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Wang YPeng HWang GTang XWang XLiu C(2023)Monitoring industrial control systems via spatio-temporal graph neural networksEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106144122:COnline publication date: 1-Jun-2023
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State-aware anomaly detection for industrial control systems

Anomaly Detection in Cybersecurity Datasets via Cooperative Co-evolution-based Feature Selection

A Statistical Analysis Framework for ICS Process Datasets

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