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2019 | OriginalPaper | Buchkapitel

ADSaS: Comprehensive Real-Time Anomaly Detection System

verfasst von : Sooyeon Lee, Huy Kang Kim

Erschienen in: Information Security Applications

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel HapticPoints: The Extended PassPoints Graphical Password

Nächstes Kapitel One-Pixel Adversarial Example that Is Safe for Friendly Deep Neural Networks

We used \(\varepsilon =0.0005\) for experiments.

Anomalies are colored with red.

The numenta anomaly benchmark. https://github.com/numenta/NAB

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Titel: ADSaS: Comprehensive Real-Time Anomaly Detection System
verfasst von: Sooyeon Lee
Huy Kang Kim
Verlag: Springer International Publishing
Buch: Information Security Applications
Print ISBN: 978-3-030-17981-6

Electronic ISBN: 978-3-030-17982-3

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-3-030-17982-3_3

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