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User activity anomaly detection and traffic prediction method based on big data analysis

A user activity and traffic prediction technology, applied in data exchange networks, digital transmission systems, electrical components, etc., can solve problems such as unfavorable user experience, service interruption, etc., achieve reasonable allocation and adjustment of network resources, improve utilization, improve The effect of accuracy

Active Publication Date: 2019-03-19
XI AN JIAOTONG UNIV
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Problems solved by technology

For example, a sudden increase in data traffic demand in a hotspot area is not conducive to user experience, and may eventually lead to service interruption in some cases

Method used

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  • User activity anomaly detection and traffic prediction method based on big data analysis
  • User activity anomaly detection and traffic prediction method based on big data analysis
  • User activity anomaly detection and traffic prediction method based on big data analysis

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Embodiment Construction

[0029] The present invention is described in further detail below in conjunction with accompanying drawing:

[0030] refer to figure 1 and figure 2 , the user activity anomaly detection and traffic prediction method based on big data analysis of the present invention comprises the following steps:

[0031] 1) Use machine learning technology to detect abnormalities in the mobile network big data CDR to identify pending abnormal areas in the mobile network big data CDR, and then obtain the area number and time period information of the pending abnormal areas;

[0032] 2) For the CDR information of mobile network big data in a certain period of time in a certain area, it is more similar to the CDR information in the continuous days of this period in this area. Although the data fluctuates irregularly, it is in a small Within the normal fluctuation range, consider the traffic activity as a set of independent and identically distributed random variable values, use historical big...

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Abstract

The invention discloses a user activity anomaly detection and traffic prediction method based on big data analysis, which comprises the following steps: 1) obtaining regional number and time period information of an undetermined abnormal region; 2) using historical big data to evaluate an empirical cumulative probability curve of the undetermined abnormal region at time period activeness; then, using the empirical cumulative probability curve to determine a traffic activeness abnormal value of each moment in a prediction region; 3) finding eight regions adjacent to the space of the predictionregion with the prediction region as the center; then, using measurement sim (i, j) of spatial similarity of two adjacent regions i, j to find a spatial similar region k of the prediction region; andfilling a vacancy value yi, t of the time sequence of the prediction region; 4) performing traffic prediction on the time sequence of the prediction region obtained in the step 3) and finishing user activity anomaly detection and traffic prediction based on big data analysis. The method can realize accurate traffic prediction of the prediction region.

Description

technical field [0001] The invention belongs to the technical field of mobile wireless networks, and relates to a method for abnormal user activity detection and flow prediction based on big data analysis. Background technique [0002] In mobile wireless networks, user experience is affected by multiple factors such as wireless coverage, traffic load, and base station configuration, and user experience may fluctuate due to changes in network conditions. For example, a sudden increase in data traffic demand in a hotspot area is not conducive to user experience, and may eventually lead to service interruption in some cases. Therefore, anomaly detection and traffic prediction of user activities are crucial for efficient allocation and adjustment of mobile wireless network resources. [0003] At present, there have been a lot of research on network anomaly detection methods. Based on the existing work, we chose the clustering-based anomaly detection method. However, our work d...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L12/26H04L29/06
CPCH04L41/147H04L43/0876H04L63/1425
Inventor 孙黎朱奇奇
Owner XI AN JIAOTONG UNIV
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