Deakin University
Browse

Fast Anomaly Detection in Multiple Multi-Dimensional Data Streams

conference contribution
posted on 2019-01-01, 00:00 authored by Hongyu Sun, Qiang He, Kewen Liao, Timos Sellis, Longkun Guo, Xuyun Zhang, Jun Shen, Feifei ChenFeifei Chen
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.

History

Volume

00

Pagination

1218-1223

Location

Los Angeles, California

Start date

2019-12-09

End date

2019-12-12

ISSN

2639-1589

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Baru C, Huan J, Khan L, Hu XH, Ak R, Tian Y, Barga R, Zaniolo C, Lee K, Ye YF

Title of proceedings

Big Data 2019 : Proceedings of the IEEE International Conference on Big Data

Event

IEEE International Conference on Big Data (2019 : Los Angeles, California)

Publisher

IEEE

Place of publication

Piscataway, N.J.

Series

IEEE International Conference on Big Data