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Ethereum phishing fraud detection method and device based on graph classification

A technology for phishing and detection methods, applied in the field of Ethereum network security, can solve the problems of complex processing, high time and computing costs, and achieve the effects of ensuring accuracy, improving expression ability, and good applicability

Active Publication Date: 2021-04-02
SUN YAT SEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] This application provides a method and device for detecting phishing fraud in Ethereum based on graph classification, which is used to solve the technical problems that the limitations of manual features in the prior art are relatively obvious, and the processing process is complicated, resulting in high time and computing costs.

Method used

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  • Ethereum phishing fraud detection method and device based on graph classification
  • Ethereum phishing fraud detection method and device based on graph classification
  • Ethereum phishing fraud detection method and device based on graph classification

Examples

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Embodiment 1

[0058] For ease of understanding, see figure 1 , Embodiment 1 of a graph classification-based Ethereum phishing fraud detection method provided by the application, including:

[0059] Step 101. Extract target nodes and preset order neighbor nodes from the Ethereum network. The target nodes include marked phishing nodes and non-fishing nodes, and the preset order neighbor nodes include first-order neighbor nodes and second-order neighbor nodes.

[0060] It should be noted that a node in the Ethereum network is an Ethereum account and has a corresponding account address; the first-order neighbor node is a related node that has direct transaction records with the target node, and the second-order neighbor node refers to a node that has a direct transaction record with the target node. The first-order neighbor nodes have related nodes with direct transaction records, and there are historical transaction record information between the associated nodes. Through the historical transa...

Embodiment 2

[0070] For ease of understanding, see figure 2 , the present application provides a second embodiment of a graph classification-based Ethereum phishing fraud detection method, including:

[0071] Step 201. Obtain historical transaction records in the Ethereum network. The historical transaction records include node account addresses, historical transaction amounts, historical transaction timestamps, historical transaction flow directions, and historical transaction times.

[0072] Step 202, extracting the target node and the preset order neighbor nodes from the historical transaction records.

[0073] It should be noted that the historical transaction records contain more information, mainly describing the dynamic changes in the transaction process. The target nodes include marked fishing nodes and non-fishing nodes, and the preset order neighbor nodes include first-order neighbor nodes and second-order neighbor nodes. The first-order neighbor nodes are related nodes that h...

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Abstract

The invention discloses an Ethereum phishing fraud detection method and device based on graph classification, and the method comprises the steps: extracting a target node and preset-order neighbor nodes from an Ethereum network, and enabling the preset-order neighbor nodes to comprise a first-order neighbor node and a second-order neighbor node; constructing a second-order transaction sub-graph network taking the target node as a center node according to the first-order neighbor node and the second-order neighbor node; refining the second-order transaction sub-graph network according to the related transaction information data of each node in the second-order transaction sub-graph network to obtain a target transaction sub-graph network; extracting features in the target transaction sub-graph network by adopting a preset graph embedding algorithm to obtain a network representation vector; and inputting the network representation vector into a preset classifier for binary classificationprocessing to obtain a target fishing node. The technical problems that in the prior art, manual feature limitation is obvious, the processing process is complex, and consequently time and operationcost is high can be solved.

Description

technical field [0001] The present application relates to the field of Ethereum network security, and in particular to a method and device for detecting phishing fraud in Ethereum based on graph classification. Background technique [0002] Blockchain is a distributed ledger technology that can ensure trusted intermediary transactions between nodes in a non-trusted environment. Blockchain can also be described as a trusted distributed database maintained based on a consensus mechanism in a peer-to-peer network. Blockchain technology has outstanding advantages in decentralization, unforgeability, anonymity, openness, etc., and because it is considered to be the next generation of disruptive core technology, it is widely used in various fields, and the most important The most well-known application is cryptocurrency. With the support of blockchain technology, blockchain platforms such as Bitcoin and Ethereum have achieved great development worldwide as emerging cryptocurrenc...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04L29/06G06K9/62G06Q40/04
CPCH04L63/1483H04L63/1425G06Q40/04G06F18/2411
Inventor 吴嘉婧袁子豪郑子彬
Owner SUN YAT SEN UNIV
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