Dark network clue detection method based on heterogeneous graph attention neural network

Abstract

The invention discloses a dark network clue detection method based on a heterogeneous graph attention neural network. The method comprises the steps 1, performing text collection on a dark network; step 2, extracting event titles, keywords and entities for the acquired dark network text information, and constructing a dynamic heterogeneous information network; step 3, performing embedding processing on the nodes in the constructed heterogeneous information network, and feature vectors of the nodes are obtained; 4, learning the graph structure of the heterogeneous information network; and step5, according to a result obtained by learning the graph structure of the heterogeneous information network, performing clue category classification on nodes in the heterogeneous information network, so that clue detection of the dark network information is completed. According to the method, an external knowledge base is used as a support, and two sets of methods are adopted to learn the graph structure of the constructed heterogeneous information network, so that a good clue detection effect is achieved.

Description

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

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

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