Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings - 2019 IEEE International Conference on Big Data, 2020, 00, pp. 1218-1223
- Issue Date:
- 2020
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| Filename | Description | Size | |||
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| 09006354.pdf | 178.26 kB |
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© 2019 IEEE. 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.
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