Frontiers of Electrical and Electronic Engineering >
Novel anomaly detection approach for telecommunication network proactive performance monitoring
Received date: 16 Oct 2008
Accepted date: 10 Nov 2008
Published date: 05 Sep 2009
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The mode of telecommunications network management is changing from “network oriented” to “subscriber oriented”. Aimed at enhancing subscribers’ feeling, proactive performance monitoring (PPM) can enable a fast fault correction by detecting anomalies designating performance degradation. In this paper, a novel anomaly detection approach is the proposed time series prediction and the associated confidence interval based on multiplicative autoregressive integrated moving average (ARIMA). Furthermore, under the assumption that the training residual is a white noise process following a normal distribution, the associated confidence interval of prediction can be figured out under any given confidence degree 1-α by constructing random variables satisfying t distribution. Experimental results verify the method’s effectiveness.
Yanhua YU , Jun WANG , Xiaosu ZHAN , Junde SONG . Novel anomaly detection approach for telecommunication network proactive performance monitoring[J]. Frontiers of Electrical and Electronic Engineering, 2009 , 4(3) : 307 -312 . DOI: 10.1007/s11460-009-0051-9
1 |
Hellerstein J L, Zhang F, Shahabuddin P. An approach to predictive detection for service management. In: Proceedings of the 6th IFIP/IEEE International Symposium on Integrated Network Management, 1999, Sloman M, Mazumdar S, Lupu E, Eds. New York: IEEE Publishing, 1999, 309-322
|
2 |
Feather F, Siewiorek D, Maxion R. Fault detection in an Ethernet network using anomaly signature matching. ACM SIGCOMM Computer Communication Review, 1993, 23(4): 279-288
|
3 |
Ho L L, Cavuto D J, Papavassiliou S, Zawadki A G. Adaptive and automated detection of service anomalies in transaction-oriented WANs: network analysis, algorithms, implementation, and deployment. IEEE Journal on Selected Areas in Communications, 2000, 18 (5): 744-757
|
4 |
Li J, Liu X X, Han Z J. Research on the ARMA-based traffic prediction algorithm for wireless sensor network. Journal of Electronics and Information Technology, 2007, 29(5): 1224-1227 (in Chinese)
|
5 |
Cadzow J A. ARMA time series modeling: an effective method. IEEE Transactions on Aerospace and Electronic Systems, 1983, AES-19(1): 49-58
|
6 |
Versace M, Bhatt R, Hinds O, Shiffer M. Predicting the exchange traded fund DIA with a combination of genetic algorithms and neural networks. Expert Systems with Applications, 2004, 27(3): 417-425
|
7 |
Mukherjee S, Osuna E, Girosi F. Nonlinear prediction of chaotic time series using support vector machines. In: Proceedings of the 1997 IEEE Workshop on Neural Networks for Signal Processing, 1997, 511-520
|
8 |
Shi Z W, Han M. Support vector echo-state machine for chaotic time-series prediction. IEEE Transactions on Neural Networks, 2007, 18(2): 359-372
|
9 |
Cao L J, Tay F E H. Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 2003, 14(6): 1506-1518
|
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