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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.

Google Scholar

[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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Chen JFeng MWirjanto TNejdl WAuer SKarras OCha MMoens MNajork M(2025)Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly DetectionProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703494(98-106)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703494
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
Tobar-Rosero ORoa-Romero ORueda-Carvajal GLeal-Piedrahita ABotero-Vega JGutierrez-Betancur SBranch-Bedoya JZapata-Madrigal G(2024)GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital SubstationsEnergies10.3390/en1723609817:23(6098)Online publication date: 4-Dec-2024
https://doi.org/10.3390/en17236098
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Index Terms

Two ICS Security Datasets and Anomaly Detection Contest on the HIL-based Augmented ICS Testbed
1. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Intrusion detection systems
  2. Network security
2. Social and professional topics
  1. Computing / technology policy
    1. Computer crime

Index terms have been assigned to the content through auto-classification.

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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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Chen JFeng MWirjanto TNejdl WAuer SKarras OCha MMoens MNajork M(2025)Prospective Multi-Graph Cohesion for Multivariate Time Series Anomaly DetectionProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703494(98-106)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703494
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
Tobar-Rosero ORoa-Romero ORueda-Carvajal GLeal-Piedrahita ABotero-Vega JGutierrez-Betancur SBranch-Bedoya JZapata-Madrigal G(2024)GOOSE Secure: A Comprehensive Dataset for In-Depth Analysis of GOOSE Spoofing Attacks in Digital SubstationsEnergies10.3390/en1723609817:23(6098)Online publication date: 4-Dec-2024
https://doi.org/10.3390/en17236098
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
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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
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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
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Tian YXu JZuo JDuan L(2024)AgileAD: Anchor-Guided Contrastive Learning with a General Data Augmentation Strategy for Time Series Anomaly Detection2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI)10.1109/ICTAI62512.2024.00086(573-577)Online publication date: 28-Oct-2024
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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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