基于神经微分方程的区块链地址风险行为识别算法

梁飞; 王瑞丽

doi:10.11959/j.issn.1000-436x.2024211

您当前的位置：

首页 >

文章列表页 >

基于神经微分方程的区块链地址风险行为识别算法

学术通信 | 更新时间：2025-01-08

- 基于神经微分方程的区块链地址风险行为识别算法
- Blockchain address risk behavior identification algorithm based on neural differential equations
- 通信学报 2024年45卷第Z1期页码：105-113
- 作者机构：
  
  1.北京市公安局经济犯罪侦查总队，北京 100061
  2.北京中科链源科技有限公司，北京 100123
- 作者简介：
  
  [ "梁飞（1989- ），男，北京人，北京市公安局经济犯罪侦查总队副高级工程师，主要研究方向为深度学习算法、区块链安全等。" ]
  [ "王瑞丽（1988- ），女，山西吕梁人，北京中科链源科技有限公司工程师，主要研究方向为区块链数据分析等。" ]
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2024211
  中图分类号： TP393
- 收稿日期：2024-09-18，
  
  纸质出版日期：2024-10-25
- 稿件说明：
移动端阅览
梁飞,王瑞丽.基于神经微分方程的区块链地址风险行为识别算法[J].通信学报,2024,45(Z1):105-113.

LIANG Fei,WANG Ruili.Blockchain address risk behavior identification algorithm based on neural differential equations[J].Journal on Communications,2024,45(Z1):105-113.
梁飞,王瑞丽.基于神经微分方程的区块链地址风险行为识别算法[J].通信学报,2024,45(Z1):105-113. DOI： 10.11959/j.issn.1000-436x.2024211.

LIANG Fei,WANG Ruili.Blockchain address risk behavior identification algorithm based on neural differential equations[J].Journal on Communications,2024,45(Z1):105-113. DOI： 10.11959/j.issn.1000-436x.2024211.

摘要

首先提出了Tgm_ODE模型，实现了波场链上的钱包地址利用USDT进行犯罪行为的识别；然后模型利用神经常微分方程模型（Neural ODE）学习到节点地址随不同的交易时间间隔而带来的特征连续变化的规律，同时引入了门控机制用于筛选出交易邻居节点地址所带给中心节点的影响强度，门控机制设计中加入了节点地址之间的交易关联性强度，最后利用自注意力机制融合不同交易时刻的节点地址特征，输出节点地址的特征表示。实验证明，Tgm_ODE模型能够有效捕捉节点地址随不规则的交易间隔时间动态变化的特征，在测试集中精准率、召回率和F1指标上优于传统的检测模型。

Abstract

First

the Tgm-ODE model was proposed

which realized the identification of criminal behavior using USDT for wallet addresses on the wavefield chain. Then a neural ordinary differential equation model (Neural ODE) was used to learn the continuous changes in the characteristics of node addresses with different transaction time intervals. At the same time

a gate mechanism was introduced to filter out the impact of neighboring node addresses on the central node. The gate mechanism design incorporated the strength of transaction correlation between node addresses. Finally

the self attention mechanism was used to fuse the node address features at different transaction times

outputting the feature representation of node addresses. Experimental results show that the Tgm-ODE model can effectively capture the dynamic changes of node addresses with irregular transaction intervals

and outperforms traditional detection models in terms of precision

recall

and F1 metrics in the test set.

关键词

Keywords

references

李大猛 , 孙杰 , 蒋照生 , 等 . 虚拟货币犯罪态势及安全治理研究综述 [J ] . 警察技术 , 2023 ( 2 ): 33 - 41 .

LI D M , SUN J , JIANG Z S , et al . Summary of research on virtual currency crime situation and security governance [J ] . Police Technology , 2023 ( 2 ): 33 - 41 .

XIA Y J , LIU J L , WU J J . Phishing detection on ethereum via attributed ego-graph embedding [J ] . IEEE Transactions on Circuits and Systems II: Express Briefs , 2022 , 69 ( 5 ): 2538 - 2542 .

CHEN Z , HUANG J , LIU S Z , et al . Multiscale feature fusion and graph convolutional network for detecting ethereum phishing scams [J ] . Electronics , 2024 , 13 ( 6 ): 1012 .

CHOI S H , BUU S J . Learning to traverse cryptocurrency transaction graphs based on transformer network for phishing scam detection [J ] . Electronics , 2024 , 13 ( 7 ): 1298 .

LIU J Y , YIN C C , WANG H , et al . Graph embedding-based money laundering detection for ethereum [J ] . Electronics , 2023 , 12 ( 14 ): 3180 .

KIM J , LEE S , KIM Y , et al . Graph learning-based blockchain phishing account detection with a heterogeneous transaction graph [J ] . Sensors , 2023 , 23 ( 1 ): 463 .

CHEN R T Q , RUBANOVA Y , BETTENCOURT J , et al . Neural ordinary differential equations [C ] // Proceedings of the 32nd International Conference on Neural Information Processing Systems . Massachusetts : MIT Press , 2018 : 6572 - 6583 .

BROUWER E D , SIMM J , ARANY A , et al . GRU-ODE-Bayes: continuous modeling of sporadically-observed time series [C ] // Proceedings of the 33rd International Conference on Neural Information Processing Systems . Massachusetts : MIT Press , 2019 : 7379 - 7390 .

崔文岳 , 谷远利 , 赵胜利 , 等 . 基于有向图卷积与门控循环单元的短时交通流预测方法 [J ] . 交通信息与安全 , 2023 , 41 ( 2 ): 121 - 128 .

CUI W Y , GU Y L , ZHAO S L , et al . A method of predicting short-term traffic flows based on a DGC-GRU model [J ] . Journal of Transport Information and Safety , 2023 , 41 ( 2 ): 121 - 128 .

周俊 , 曹月恬 , 胡斌斌 , 等 . 基于实时动态图联合学习框架的金融交易风控技术 [J ] . 电子学报 , 2023 , 51 ( 10 ): 2801 - 2811 .

ZHOU J , CAO Y T , HU B B , et al . Real-time dynamic graph unified learning framework for financial transaction risk management [J ] . Acta Electronica Sinica , 2023 , 51 ( 10 ): 2801 - 2811 .

浏览量

207

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据