Dynamic network abnormal link behavior detection method and system

A dynamic network and detection method technology, applied in transmission systems, digital transmission systems, data exchange networks, etc., can solve the problems that abnormal link identification results are not very persuasive, and the implicit relationship is insufficiently considered, so as to make up for the relationship consideration. Insufficient and accurate identification of the effect

Active Publication Date: 2018-09-14
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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Problems solved by technology

[0003] Most of the existing network abnormal link detection technologies only evaluate the relationship between nodes through the number of historical links,

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  • Dynamic network abnormal link behavior detection method and system
  • Dynamic network abnormal link behavior detection method and system
  • Dynamic network abnormal link behavior detection method and system

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Embodiment Construction

[0045] In order to make the purpose, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the present invention Examples, not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts fall within the protection scope of the present invention.

[0046] The technical terms appearing in the embodiments of the present invention are explained below.

[0047] Dynamic network: Unlike static networks, dynamic networks are constantly changing over time, manifested as nodes in the network graph and edges between nodes appearing or disappearing over time. A dynamic network containing n time slices is expressed as G={G 1 ,G 2 ...

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Abstract

The invention provides a dynamic network abnormal link behavior detection method and a system thereof. The method comprises the following steps of: determining, according to the first T time slice networks {G1, G2, ..., GT-1, GT}, a historical network Ghistory of the current time slice network GT+1; performing network representation learning on the historical network Ghistory by using a network representation learning method to determine a new historical network G'history; determining a similarity adj (i, j) between the network node pairs (i, j) according to the distance dij between the network node pairs (i, j) in the new historical network G'history; and if it is determined that the proximity adj(i, j) is less than the preset abnormality determination threshold, determining that the link(i, j) between the node pairs (i, j) in the current time slice network GT+1 is an abnormal link behavior. The system includes a historical network construction module, a network representation learning module, a proximity determination module, and an abnormal link determination module. The invention may obtain the degree of similarity between nodes, and is further capable of more accurately identifying the abnormal link behavior of the dynamic network according to the similarity between the nodes.

Description

technical field [0001] The present invention relates to the technical field of dynamic networks, in particular to a method and system for detecting abnormal link behaviors in dynamic networks. Background technique [0002] In the process of dynamic network evolution, there will be link behaviors between nodes at any time. The purpose of abnormal link behavior detection is to identify which link behaviors are abnormal among many network links. Abnormal link behavior is often the direct or indirect cause of abnormal changes in the network. For example, the short circuit of some links in the power network may cause the failure of the entire power grid. A host on the Internet may be infected with a network virus when visiting a malicious website. spread to cause damage on a larger scale. Therefore, the identification of abnormal links is very necessary. [0003] Most of the existing network abnormal link detection technologies only evaluate the relationship between nodes throu...

Claims

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Application Information

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IPC IPC(8): H04L12/24H04L12/26
CPCH04L41/14H04L41/145H04L43/08H04L43/0805
Inventor 刘琰孙通刘楝罗向阳南煜陶致远
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
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