skip to main content
10.1145/3440943.3444353acmconferencesArticle/Chapter ViewAbstractPublication PagesiceaConference Proceedingsconference-collections
short-paper

Anomaly detection in time-series data environment

Published: 27 September 2021 Publication History

Abstract

Typical label data detect anomaly due to the relationship between inputs and labels, but time-series data are more demanding in detecting anomalies because they detect anomaly based on time-varying values. To solve this problem, this paper proposed Stacked-Autoencoder based data detection technique with ICS dataset among time series data. The Loss value was calculated as CDF and determined to be a suspicious event if it was greater than the arbitrarily specified threshold value. The experiment was carried out by designating 0.5, 0.7, 0.9 and 0.98, and 0.98 showed the best result with an accuracy of about 96%.

References

[1]
Global Industrial Control Systems Security Market 2016-2020, https://www.researchandmarkets.com/, 2016
[2]
Teng, M. (2010, December). Anomaly detection on time series. In 2010 IEEE International Conference on Progress in Informatics and Computing (Vol.1, pp. 603--608). IEEE.
[3]
Hyuk-ki Shin, Woomyo Lee, Jeong-Han Yun, and Hyoungchun Kim, HAI 1.0: HIL-based Augmented ICS Security Dataset, 13th, USENIX Workshop on Cyber Security Experimentation and Test, 2020.
[4]
Yin, C., Zhang, S., Wang, J., & Xiong, N. N. (2020). Anomaly Detection Based on Convolutional Recurrent Autoencoder for IoT Time Series. IEEE Transactions on Systems, Man, and Cybernetics: Systems.
[5]
Kim, S., Hwang, C., & Lee, T. (2020). Anomaly Based Unknown Intrusion Detection in Endpoint Environments. Electronics, 9(6), 1022.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ACM ICEA '20: Proceedings of the 2020 ACM International Conference on Intelligent Computing and its Emerging Applications
December 2020
219 pages
ISBN:9781450383042
DOI:10.1145/3440943
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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. HAI
  2. Industrial Control System
  3. Point Anomaly Detection
  4. Stacked-Autoencoder

Qualifiers

  • Short-paper
  • Research
  • Refereed limited

Conference

ACM ICEA '20
Sponsor:

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 145
    Total Downloads
  • Downloads (Last 12 months)25
  • Downloads (Last 6 weeks)0
Reflects downloads up to 15 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media