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Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (icas-icns'05)
Network Anomaly Detection Using Time Series Analysis
Papeete, Tahiti
October 23-October 28
ISBN: 0-7695-2450-8
ASCII Text
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Qingtao Wu, Zhiqing Shao, "Network Anomaly Detection Using Time Series Analysis," Autonomic and Autonomous Systems and International Conference on Networking and Services, Joint International Conference on, pp. 42, Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (icas-icns'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICAS-ICNS.2005.69,
author = {Qingtao Wu and Zhiqing Shao},
title = {Network Anomaly Detection Using Time Series Analysis},
journal ={Autonomic and Autonomous Systems and International Conference on Networking and Services, Joint International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2450-8},
pages = {42},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICAS-ICNS.2005.69},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Autonomic and Autonomous Systems and International Conference on Networking and Services, Joint International Conference on
TI - Network Anomaly Detection Using Time Series Analysis
SN - 0-7695-2450-8
SP
EP
A1 - Qingtao Wu,
A1 - Zhiqing Shao,
PY - 2005
KW - null
VL - 0
JA - Autonomic and Autonomous Systems and International Conference on Networking and Services, Joint International Conference on
ER -
 
Qingtao Wu, East China University of Science and Technology
Zhiqing Shao, East China University of Science and Technology
This paper presents a method of detecting network anomalies by analyzing the abrupt change of time series data obtained from Management Information Base (MIB) variables. The method applies the Auto- Regressive (AR) process to model the abrupt change of time series data, and performs sequential hypothesis test to detect the anomalies. With time correlation and location correlation, the method determines not only the presence of anomalous activity, but also its occurring time and location. The experimental results show that the proposed method performs well in detecting the traffic-related Anomalies
Citation:
Qingtao Wu, Zhiqing Shao, "Network Anomaly Detection Using Time Series Analysis," icas-icns, pp.42, Joint International Conference on Autonomic and Autonomous Systems and International Conference on Networking and Services - (icas-icns'05), 2005
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