Novel anomaly detection approach for telecommunication network proactive performance monitoring

doi:10.1007/s11460-009-0051-9

Front Elect Electr Eng Chin

2009, Vol. 4

Issue (3) : 307-312 https://doi.org/10.1007/s11460-009-0051-9

RESEARCH ARTICLE

Novel anomaly detection approach for telecommunication network proactive performance monitoring

Yanhua YU(

), Jun WANG, Xiaosu ZHAN, Junde SONG

School of Computer Science and Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China

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Abstract

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.

Keywords proactive performance monitoring (PPM) anomaly detection time series prediction autoregressive integrated moving average (ARIMA) white noise confidence interval

Corresponding Author(s): YU Yanhua,Email:yhyu_bupt@sina.com.cn

Issue Date: 05 September 2009

Cite this article:

Xiaosu ZHAN,Junde SONG,Yanhua YU, et al. Novel anomaly detection approach for telecommunication network proactive performance monitoring[J]. Front Elect Electr Eng Chin, 2009, 4(3): 307-312.

URL:

https://academic.hep.com.cn/fee/EN/10.1007/s11460-009-0051-9
https://academic.hep.com.cn/fee/EN/Y2009/V4/I3/307

Tab.1 Hourly traffic load series at 9:00 from 2007-7-4 to 2007-8-17

Fig.1 Autocorrelation function of {}

Fig.2 Autocorrelation function of {}

Tab.2 Prediction for 9:00 2007-8-10 till 9:00 2007-8-17

Fig.3 Prediction value, actual value, thresholds for traffic from 2007-8-10 to 2007-8-17

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