Fast anomaly detection in multiple multi-dimensional data streams
Conference paper
Sun, Hongyu, He, Qiang, Liao, Kewen, Sellis, Timos, Guo, Longkun, Zhang, Xuyun, Shen, Jun and Chen, Feifei. (2019). Fast anomaly detection in multiple multi-dimensional data streams. 2019 IEEE International Conference on Big Data. Los Angeles, California, United States of America 09 - 12 Dec 2019 IEEE Computer Society. pp. 1218-1223 https://doi.org/10.1109/BigData47090.2019.9006354
Authors | Sun, Hongyu, He, Qiang, Liao, Kewen, Sellis, Timos, Guo, Longkun, Zhang, Xuyun, Shen, Jun and Chen, Feifei |
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Type | Conference paper |
Abstract | Multiple multi-dimensional data streams are ubiquitous in the modern world, such as IoT applications, GIS applications and social networks. Detecting anomalies in such data streams in real-time is an important and challenging task. It is able to provide valuable information from data and then assists decision-making. However, exiting approaches for anomaly detection in multi-dimensional data streams have not properly considered the correlations among multiple multi-dimensional streams. Moreover, for multi-dimensional streaming data, online detection speed is often an important concern. In this paper, we propose a fast yet effective anomaly detection approach in multiple multi-dimensional data streams. This is based on a combination of ideas, i.e., stream pre-processing, locality sensitive hashing and dynamic isolation forest. Experiments on real datasets demonstrate that our approach achieves a magnitude increase in its efficiency compared with state-of-the-art approaches while maintaining competitive detection accuracy. |
Keywords | anomaly detection; multi-dimensional data streams; locality sensitive hashing; isolation forest; unsupervised learning |
Year | 2019 |
Publisher | IEEE Computer Society |
Digital Object Identifier (DOI) | https://doi.org/10.1109/BigData47090.2019.9006354 |
Scopus EID | 2-s2.0-85081339393 |
Publisher's version | License All rights reserved File Access Level Controlled |
Book title | 2019 IEEE International Conference on Big Data : Proceedings : Dec 9 - Dec 12, 2019, Los Angeles, CA, USA |
Page range | 1218-1223 |
ISBN | 9781728108582 |
9781728108575 | |
9781728108599 | |
Web address (URL) of conference proceedings | https://doi.org/10.1109/BigData47090.2019 |
Output status | Published |
Publication dates | |
Online | 2019 |
Publication process dates | |
Deposited | 13 Oct 2023 |
https://acuresearchbank.acu.edu.au/item/8zvw2/fast-anomaly-detection-in-multiple-multi-dimensional-data-streams
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