ADSaS: Comprehensive real-time anomaly detection system

Sooyeon Lee, Huy Kang Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Since with massive data growth, the need for autonomous and generic anomaly detection system is increased. However, developing one stand-alone generic anomaly detection system that is accurate and fast is still a challenge. In this paper, we propose conventional time-series analysis approaches, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model and Seasonal Trend decomposition using Loess (STL), to detect complex and various anomalies. Usually, SARIMA and STL are used only for stationary and periodic time-series, but by combining, we show they can detect anomalies with high accuracy for data that is even noisy and non-periodic. We compared the algorithm to Long Short Term Memory (LSTM), a deep-learning-based algorithm used for anomaly detection system. We used a total of seven real-world datasets and four artificial datasets with different time-series properties to verify the performance of the proposed algorithm.

Original languageEnglish
Title of host publicationInformation Security Applications - 19th International Conference, WISA 2018, Revised Selected Papers
EditorsBrent ByungHoon Kang, JinSoo Jang
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783030179816
Publication statusPublished - 2019
Event19th World International Conference on Information Security and Application, WISA 2018 - Jeju Island, Korea, Republic of
Duration: 2018 Aug 232018 Aug 25

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11402 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference19th World International Conference on Information Security and Application, WISA 2018
Country/TerritoryKorea, Republic of
CityJeju Island

Bibliographical note

Funding Information:
Acknowledgements. This work was supported under the framework of international cooperation program managed by National Research Foundation of Korea (No. 2017K1A3A1A17 092614).

Publisher Copyright:
© Springer Nature Switzerland AG 2019.


  • Anomaly detection
  • Data stream
  • Real-time
  • STL

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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