Diffusion wavelet-based anomaly detection in networks

No Thumbnail Available
File version
Author(s)
Tian, H
Ding, M
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
2017
Size
File type(s)
Location

Guangzhou, China

License
Abstract

Traffic Matrix (TM) can contain information about irregular network topology structure and depict the traffic characteristics of global network. It is a critical parameter to network traffic engineering and attracts significant research interests. Diffusion Wavelet (DW) can perform an effective Multi-Resolution Analysis (MRA)on TM in both temporaland space domains because it intrinsically adapts to the underlying network structure. This paper shows how to apply DW to TM analysis and anomaly detection. By comparing with other anomaly detection methods, it is confirmed thatour method can detect anomaly effectively due to combining with the analysis results by DW.

Journal Title
Conference Title

2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)

Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject

Applied computing

Persistent link to this record
Citation

Tian, H; Ding, M, Diffusion wavelet-based anomaly detection in networks, 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), 2017, pp. 382-386