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.
| Original language | English |
|---|---|
| Title of host publication | Information Security Applications - 19th International Conference, WISA 2018, Revised Selected Papers |
| Editors | Brent ByungHoon Kang, JinSoo Jang |
| Publisher | Springer Verlag |
| Pages | 29-41 |
| Number of pages | 13 |
| ISBN (Print) | 9783030179816 |
| DOIs | |
| Publication status | Published - 2019 Jan 1 |
| Event | 19th World International Conference on Information Security and Application, WISA 2018 - Jeju Island, Korea, Republic of Duration: 2018 Aug 23 → 2018 Aug 25 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11402 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 19th World International Conference on Information Security and Application, WISA 2018 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 18/8/23 → 18/8/25 |
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Keywords
- Anomaly detection
- Data stream
- Real-time
- SARIMA
- STL
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
ADSaS : Comprehensive real-time anomaly detection system. / Lee, Sooyeon; Kim, Huy Kang.
Information Security Applications - 19th International Conference, WISA 2018, Revised Selected Papers. ed. / Brent ByungHoon Kang; JinSoo Jang. Springer Verlag, 2019. p. 29-41 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11402 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - ADSaS
T2 - Comprehensive real-time anomaly detection system
AU - Lee, Sooyeon
AU - Kim, Huy Kang
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Anomaly detection
KW - Data stream
KW - Real-time
KW - SARIMA
KW - STL
UR - http://www.scopus.com/inward/record.url?scp=85065013521&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85065013521&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-17982-3_3
DO - 10.1007/978-3-030-17982-3_3
M3 - Conference contribution
SN - 9783030179816
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 41
BT - Information Security Applications - 19th International Conference, WISA 2018, Revised Selected Papers
A2 - Kang, Brent ByungHoon
A2 - Jang, JinSoo
PB - Springer Verlag
ER -