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Fast anomaly detection in multiple multi-dimensional data streams
Sun, Hongyu He, Qiang Liao, Kewen Sellis, Timos Guo, Longkun Zhang, Xuyun Shen, Jun Chen, Feifei
Sun, Hongyu
He, Qiang
Liao, Kewen
Sellis, Timos
Guo, Longkun
Zhang, Xuyun
Shen, Jun
Chen, Feifei
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
Date
2019
Type
Conference paper
Journal
Book
2019 IEEE International Conference on Big Data : Proceedings : Dec 9 - Dec 12, 2019, Los Angeles, CA, USA
Volume
Issue
Page Range
1218-1223
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Open Access Status
License
All rights reserved
File Access
Controlled