A darknet clue detection method based on heterogeneous graph attention neural network

A technology of neural network and detection method, which is applied in dark web clue detection and learning field of heterogeneous information network graph structure, to achieve good clue detection effect

Active Publication Date: 2022-08-05
NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Can not make good use of external knowledge to help detect dark web clues and learn the hidden relationship between dark web information from different sources

Method used

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  • A darknet clue detection method based on heterogeneous graph attention neural network
  • A darknet clue detection method based on heterogeneous graph attention neural network
  • A darknet clue detection method based on heterogeneous graph attention neural network

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Embodiment Construction

[0058] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0059] The technical solutions of the present invention will be described in detail below with specific examples.

[0060] The invention provides a darknet clue detection system based on heterogeneous graph attention neural network.

[0061] image 3 It is a schematic flowchart of Embodiment 1 of the dark web clue detection system of the present invention, as shown in image 3 As shown, the method for detecting dark web clues in thi...

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Abstract

The invention discloses a darknet clue detection method based on heterogeneous graph attention neural network. Extract and construct a dynamic heterogeneous information network; step 3, perform embedding processing on the nodes in the constructed heterogeneous information network, and obtain feature vectors of each node; step 4, learn the graph structure of the heterogeneous information network; step 5 . According to the result of learning the graph structure of the heterogeneous information network, the nodes in the heterogeneous information network are classified into clue categories, so as to complete the clue detection of darknet information. The invention utilizes the external knowledge base as the support, and adopts two sets of methods to learn the graph structure of the constructed heterogeneous information network, and has good clue detection effect.

Description

technical field [0001] The invention relates to machine learning technology, in particular to a darknet clue detection method based on heterogeneous graph attention neural network, which belongs to the technology of learning heterogeneous information network graph structure. Background technique [0002] There are a large number of clues that threaten public security, financial security, and information security on the dark web such as Tor, I2P, and ZeroNet. Detecting and identifying threat clues in the dark web is of great value in preventing the above risks. [0003] The existing darknet clue detection system usually performs structured processing, automatic language translation, and automatic noise reduction processing on the collected darknet information. Categorize common threat clues to build an automated threat intelligence clue recognition model. [0004] The above method lacks the utilization of external text data knowledge base and network data knowledge base and ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/953G06F16/951G06N3/08G06N3/04
CPCG06F16/953G06F16/951G06N3/08G06N3/045
Inventor 陈志鹏刘春阳张丽姜文华张旭孙旻
Owner NAT COMP NETWORK & INFORMATION SECURITY MANAGEMENT CENT
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