@inproceedings{59f44783451d4387b8318e3a377e6e02,
title = "ADSaS: Comprehensive real-time anomaly detection system",
abstract = "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.",
keywords = "Anomaly detection, Data stream, Real-time, SARIMA, STL",
author = "Sooyeon Lee and Kim, {Huy Kang}",
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: {\textcopyright} Springer Nature Switzerland AG 2019.; 19th World International Conference on Information Security and Application, WISA 2018 ; Conference date: 23-08-2018 Through 25-08-2018",
year = "2019",
doi = "10.1007/978-3-030-17982-3_3",
language = "English",
isbn = "9783030179816",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "29--41",
editor = "Kang, {Brent ByungHoon} and JinSoo Jang",
booktitle = "Information Security Applications - 19th International Conference, WISA 2018, Revised Selected Papers",
}