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Method and system for detecting abnormal events of networks

A technology for network anomaly and event detection, applied in network topology, electrical components, wireless communication, etc.

Active Publication Date: 2014-03-26
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The present invention provides a method and system for detecting network abnormal events to solve the technical problem in the prior art that the time-space correlation of network abnormal events cannot be comprehensively considered

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  • Method and system for detecting abnormal events of networks
  • Method and system for detecting abnormal events of networks
  • Method and system for detecting abnormal events of networks

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Embodiment Construction

[0051]In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0052] In the process of network anomaly event detection, the spatio-temporal correlation is very obvious, such as figure 1 Shown: When at time t, there is a network abnormal event burst, which affects the readings of two sensor nodes in the gray area, as shown in the left figure; at time t+1 after a period of time, the event spreads around and exp...

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Abstract

The invention provides a method and a system for detecting abnormal events of networks. The method includes learning observed state sequences and implied state sequences in actual deployment of the networks to acquire optimal parameters; combining the optimal parameters to calculate implied detection sequences according to inputted observed detection sequences and detecting the abnormal events of the networks. The observed state sequences and implied state sequences are functions of spatial coordinate values of time slice and sensor nodes. The method and the system have the advantages that the various optimal parameters in random fields under dynamic conditions can be acquired via offline learning procedures and then are combined with one another to detect the abnormal events in an online manner according to the inputted observed detection sequences, so that the abnormal events of the networks within a period of time can be known; the various sequences are temporal and spatial functions, and accordingly the abnormal events of the networks can be temporally and spatially detected in a unified manner by the aid of spatial-temporal mode sets.

Description

technical field [0001] The invention relates to the field of wireless sensor networks, in particular to a method and system for detecting network abnormal events. Background technique [0002] With the rapid development of embedded systems and the increasing maturity of wireless communication technology, wireless sensor networks are widely used in ecological scientific observation, environmental detection, disaster early warning, and national defense and military fields. In these practical applications, automated anomaly event detection functions are essential. Generally speaking, in order to take corresponding remedial measures for network abnormalities as soon as possible, we need to detect all sudden events that affect system applications in real time, including physical phenomena, software failures, etc., and then give early warning prompts. [0003] Traditional network anomaly event detection methods can be roughly divided into three categories: (1) Threshold-based met...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/04H04W84/18
Inventor 马强刘克彬刘云浩
Owner TSINGHUA UNIV
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