Abstract
Significant progress has been developed previously in modeling network traffic with fractals. These developments have given rise to a new insight and physical understanding of the effects of scaling properties in measured network traffic. Among them, multi-fractal models fit measured data more naturally. This paper takes advantage of multi-fractal model to detect fault in network traffic. Faults in a self-similar traffic destroy the singularity structure at the time points they occur, resulting in a significant deviation from those of normal traffic. For fault detection, we measure the degree of deviation of singularity exponent at every time segment through a deviation indicator Q based on structure function Sj(q). Since faults usually bring out abnormal bursts against natural ones in traffic, the proposed algorithm is thereby able to detect them. As demonstrated on simulated and real network traffic data, this algorithm can detect abnormal traffic loads with natural traffic bursts in the background.
Original language | English |
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Title of host publication | 2002 International Conference on Communications, Circuits and Systems and West Sino Exposition, ICCCAS 2002 - Proceedings |
Publisher | IEEE |
Pages | 695-699 |
Number of pages | 5 |
ISBN (Electronic) | 0780375475, 9780780375475 |
DOIs | |
Publication status | Published - 1 Jan 2002 |
Externally published | Yes |
Event | 1st International Conference on Communications, Circuits and Systems, ICCCAS 2002 - Tibet Hotel, Chengdu, China Duration: 29 Jun 2002 → 1 Jul 2002 |
Conference
Conference | 1st International Conference on Communications, Circuits and Systems, ICCCAS 2002 |
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Country/Territory | China |
City | Chengdu |
Period | 29/06/02 → 1/07/02 |
Keywords
- fault detection
- multi-fractal
- network traffic
- wavelet
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Networks and Communications
- Control and Systems Engineering
- Electrical and Electronic Engineering