Abnormal group detection method based on weighted dynamic network representation learning

A technology of dynamic network and detection method, applied in the direction of neural learning method, biological neural network model, instrument, etc., can solve the problems of not being able to learn the corresponding relationship between edges and weights well, and being unable to effectively identify abnormal weights, etc.

Active Publication Date: 2020-05-08
PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
View PDF2 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention aims at the problem that the existing network representation learning method cannot learn the corresponding relationship between edges and weights well when facing the weighted dynamic

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Abnormal group detection method based on weighted dynamic network representation learning
  • Abnormal group detection method based on weighted dynamic network representation learning
  • Abnormal group detection method based on weighted dynamic network representation learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0061] For a better understanding of the present invention, the meanings of some nouns appearing in the present invention are explained:

[0062] Weighted dynamic network: Weighted dynamic network is a weighted network that changes over time. A dynamic network containing n time slices is expressed as G={G 1 ,G 2 ,...,G t ,G t+1 ,...,G n}, where the tth time slice network G t =(V t ,E t ,W t ), V t is the set of vertices in the network, E t Represents the relationship between vertices for the set of edges, W t is the set of edge weights.

[0063] Weight exception: Given a dynamic network G={G 1 ,G 2 ,...,G t ,G t+1 ,...,G n}, for any time slice network G t =(V t ,E t ,W t ), for any edge e i ∈ E t , e i ={frm,to,w i}, where frm and to are the two endpoints of the edge, w t is the weight of the current edge, within the range of n time slices, the normal weight range of the edge with frm and to as endpoints is [w l ,w h ], if w i l or w i >w h then th...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention belongs to the technical field of dynamic network anomaly detection, and discloses an abnormal group detection method based on weighted dynamic network representation learning, which comprises the following steps: step 1, constructing a weighted dynamic network representation learning model based on a deep self-encoding neural network; 2, performing abnormal link identification basedon the constructed weighted dynamic network representation learning model to obtain an abnormal link set; and 3, constructing a full-connection neural network model based on the abnormal link set, and performing abnormal group detection through the full-connection neural network model. According to the method, the exception link is combined with the full-connection neural network exception detection model, the application range of the method is expanded on the basis of the exception link, experimental verification is carried out on a secure mail data set and an AS-level Internet data set, andexperimental results show that the method has a good exception group detection effect.

Description

technical field [0001] The invention belongs to the technical field of dynamic network anomaly detection, in particular to an abnormal group detection method based on weighted dynamic network representation learning. Background technique [0002] With the rapid development of network technology and the widespread popularization of computers and mobile smart terminals, the network has greatly changed people's work and life. At the same time, the scale of the network has become larger and more complex. This makes it more and more difficult to detect anomalies in dynamic networks. It is difficult to fully capture the structural features in the graph based on the existing statistical methods of graph structure features. How to effectively identify abnormal groups in changing networks is a current research hotspot. [0003] The basic idea of ​​network representation learning is to convert the nodes in the network into multi-dimensional vector representations through a series of ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/23G06F18/214
Inventor 冯昊刘琰周资乔钟凤喆王博
Owner PLA STRATEGIC SUPPORT FORCE INFORMATION ENG UNIV PLA SSF IEU
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap