基于图神经网络的账户余额模型区块链地址分类方法

李致远; 徐丙磊; 周颖仪

doi:10.11959/j.issn.1000-436x.2023173

您当前的位置：

首页 >

文章列表页 >

基于图神经网络的账户余额模型区块链地址分类方法

学术论文 | 更新时间：2024-06-06

- 基于图神经网络的账户余额模型区块链地址分类方法
- Graph neural network-based address classification method for account balance model blockchain
- 通信学报 2023年44卷第9期页码：115-126
- 作者机构：
  
  1. 江苏大学计算机科学与通信工程学院，江苏镇江 212013
  2. 江苏省工业网络安全技术重点实验室，江苏镇江 212013
  3. 江苏省泛在数据智能感知与分析应用工程研究中心，江苏镇江 212013
- 作者简介：
  
  [ "李致远（1981- ），男，河南开封人，博士，江苏大学副教授、硕士生导师，主要研究方向为区块链匿名交易追踪、物联网和软件定义网络及安全" ]
  [ "徐丙磊（1997- ），男，山东莱阳人，江苏大学硕士生，主要研究方向为区块链匿名可追踪、区块链地址分类" ]
  [ "周颖仪（2000- ），女，江苏苏州人，江苏大学硕士生，主要研究方向为区块链交易网络构建与链路预测" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2020YFB1005503);江苏省自然科学基金资助项目(BK20201415)
- DOI：10.11959/j.issn.1000-436x.2023173
  中图分类号： TP301
- 网络出版日期：2023-09，
  
  纸质出版日期：2023-09-25
- 稿件说明：
移动端阅览
李致远, 徐丙磊, 周颖仪. 基于图神经网络的账户余额模型区块链地址分类方法[J]. 通信学报, 2023,44(9):115-126.

Zhiyuan LI, Binglei XU, Yingyi ZHOU. Graph neural network-based address classification method for account balance model blockchain[J]. Journal on communications, 2023, 44(9): 115-126.
李致远, 徐丙磊, 周颖仪. 基于图神经网络的账户余额模型区块链地址分类方法[J]. 通信学报, 2023,44(9):115-126. DOI： 10.11959/j.issn.1000-436x.2023173.

Zhiyuan LI, Binglei XU, Yingyi ZHOU. Graph neural network-based address classification method for account balance model blockchain[J]. Journal on communications, 2023, 44(9): 115-126. DOI： 10.11959/j.issn.1000-436x.2023173.

摘要

为了监管账户余额模型公链上的交易，有必要对该类区块链上的交易进行地址分类研究。基于此，提出了一种基于图神经网络的账户余额模型区块链地址分类方法（简称 AJKGS-ABCM）以实现区块链地址的分类，为区块链交易追踪提供有效的支持。该方法将区块链交易数据建模为图结构，以地址为节点，交易为边，提出AJK-GraphSAGE 算法学习图的嵌入表示，模型的输入只需要节点及其采样的邻居节点集合。同时，模型引入注意力机制及跳跃知识结合策略，自适应地为不同层的表示分配权重，并在不同层间共享信息，提高了训练速度和泛化能力。最后进行了实验对比，结果表明该模型在准确度、召回率和F1分数上性能优于其他方法。

Abstract

To regulate the transactional activities on the public blockchain involving account balance models

it is necessary to conduct research on address classification for transactions on such blockchains.A blockchain address classification method

named AJKGS-ABCM (attention jumping knowledge graph SAGE account-based blockchain classification model)

was proposed to categorize blockchain addresses

providing effective support for blockchain transaction tracking.Blockchain transaction data was represented as a graph structure

with addressed as nodes and transactions as edges.The AJK-GraphSAGE algorithm was introduced to learn embedded representations of the graph

where the model’s input required only nodes and their sampled neighboring node sets.Simultaneously

attention mechanisms and skip-connection knowledge integration strategies were incorporated into the model

allowing for adaptive weight allocation across different layers and information sharing between various levels

thereby enhancing training speed and generalization capabilities.Finally

experimental comparisons are conducted

demonstrating superior performance in terms of accuracy

recall

and F1 score compared to other methods.

关键词

Keywords

references

BENDIAB G , HAMEURLAINE A , GERMANOS G , et al . Autonomous vehicles security:challenges and solutions using blockchain and artificial intelligence [J ] . IEEE Transactions on Intelligent Transportation Systems , 2023 , 24 ( 4 ): 3614 - 3637 .

FENG Q , HE D , ZEADALLY S , et al . A survey on privacy protection in blockchain system [J ] . Journal of Network and Computer Applications , 2019 , 126 : 45 - 58 .

HATHALIYA J J , MODI H , GUPTA R , et al . Deep learning and blockchain-based essential and parkinson tremor classification scheme [C ] // Proceedings of 2022 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) . Piscataway:IEEE Press , 2022 : 1 - 6 .

LI Z Y , HE E H . Graph neural network-based bitcoin transaction tracking model [J ] . IEEE Access , 2023 , 11 : 62109 - 62120 .

MONAMO P , MARIVATE V , TWALA B . Unsupervised learning for robust Bitcoin fraud detection [C ] // Proceedings of Information Security for South Africa (ISSA) . Piscataway:IEEE Press , 2017 : 129 - 134 .

TOYODA K , OHTSUKI T , MATHIOPOULOS P T . Multi-class bitcoin-enabled service identification based on transaction history summarization [C ] // Proceedings of IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber,Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) . Piscataway:IEEE Press , 2019 : 1153 - 1160 .

LIN Y J , WU P W , HSU C H , et al . An evaluation of bitcoin address classification based on transaction history summarization [C ] // Proceedings of 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC) . Piscataway:IEEE Press , 2019 : 302 - 310 .

BARTOLETTI M , PES B , SERUSI S . Data mining for detecting bitcoin ponzi schemes [C ] // Proceedings of 2018 Crypto Valley Conference on Blockchain Technology (CVCBT) . Piscataway:IEEE Press , 2018 : 75 - 84 .

LI Y , CAI Y , TIAN H , et al . Identifying illicit addresses in bitcoin network [C ] // International Conference on Blockchain and Trustworthy Systems . Singapore:Springer , 2020 : 99 - 111 .

HU T , LIU X , CHEN T , et al . Transaction-based classification and detection approach for Ethereum smart contract [J ] . Information Processing ＆ Management , 2021 , 58 ( 2 ): 102462 .

WU J J , YUAN Q , LIN D , et al . Who are the phishers？ phishing scam detection on ethereum via network embedding [J ] . IEEE Transactions on Systems,Man,and Cybernetics:Systems , 2022 , 52 ( 2 ): 1156 - 1166 .

CHEN W , GUO X , CHEN Z , et al . Phishing scam detection on ethereum:towards financial security for blockchain ecosystem [C ] // Proceedings of International Joint Conference on Artificial Intelligence . Piscataway:IEEE Press , 2020 : 4456 - 4462 .

CHEN L , PENG J Y , LIU Y , et al . Phishing scams detection in ethereum transaction network [J ] . ACM Transactions on Internet Technology , 2021 , 21 ( 1 ): 1 - 16 .

KE G , MENG Q , FINLEY T , et al . Lightgbm:a highly efficient gradient boosting decision tree [C ] // Advances in Neural Information Processing Systems . San Francisco:Morgan Kaufmann Press , 2017 :30.

YUAN Q , HUANG B Y , ZHANG J , et al . Detecting phishing scams on ethereum based on transaction records [C ] // Proceedings of 2020 IEEE International Symposium on Circuits and Systems (ISCAS) . Piscataway:IEEE Press , 2020 : 1 - 5 .

ZHOU J , HU C , GONG S , et al . BlockGC:a joint learning framework for account identity inference on blockchain with graph contrast [J ] . arXiv Preprint,arXiv:2112.03659 , 2021 .

LIU J L , ZHENG J T , WU J J , et al . FA-GNN:filter and augment graph neural networks for account classification in ethereum [J ] . IEEE Transactions on Network Science and Engineering , 2022 , 9 ( 4 ): 2579 - 2588 .

KIPF T N , WELLING M . Semi-supervised classification with graph convolutional networks [J ] . arXiv Preprint,arXiv:1609.02907 , 2016 .

HAMILTON W L , YING R , LESKOVEC J . Inductive representation learning on large graphs [J ] . arXiv Preprint,arXiv:1706.02216 , 2017 .

GUO G D , WANG H , BELL D A , et al . KNN model-based approach in classification [C ] // Proceedings of OTM Confederated International Conferences .[S.l.:s.n. ] , 2003 : 986 - 996 .

PEROZZI B , AL-RFOU R , SKIENA S . DeepWalk:online learning of social representations [C ] // Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2014 : 701 - 710 .

GROVER A , LESKOVEC J . Node2Vec:scalable feature learning for networks [J ] . KDD:Proceedings International Conference on Knowledge Discovery ＆ Data Mining , 2016 , 2016 : 855 - 864 .

RIBEIRO L F R , SAVERESE P H P , FIGUEIREDO D R . Struc2Vec:learning node representations from structural identity [C ] // Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . New York:ACM Press , 2017 : 385 - 394 .

浏览量

393

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

超大规模太赫兹系统深度学习信道估计算法

基于SVM-RFE与Transformer-TBAM的高校邮件分析研究

面向WSN异常节点检测的融合重构机制与对比学习方法

基于预训练与新型时序图神经网络的智能合约漏洞检测方法

基于局部增强的中文医疗命名实体识别模型