基于多特征自适应融合的区块链异常交易检测方法

朱会娟; 陈锦富; 李致远; 殷尚男

doi:10.11959/j.issn.1000-436x.2021030

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基于多特征自适应融合的区块链异常交易检测方法

学术论文 | 更新时间：2024-06-06

- 基于多特征自适应融合的区块链异常交易检测方法
- Block-chain abnormal transaction detection method based on adaptive multi-feature fusion
- 通信学报 2021年42卷第5期页码：41-50
- 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
- 作者简介：
  
  [ "朱会娟（1984- ），女，河南洛阳人，博士，江苏大学副教授、硕士生导师，主要研究方向为区块链、网络安全及人工智能" ]
  [ "陈锦富（1978- ），男，江西信丰人，博士，江苏大学教授、博士生导师，主要研究方向为可信软件及软件安全" ]
  [ "李致远（1981- ），男，河南开封人，博士，江苏大学副教授、硕士生导师，主要研究方向为物联网、大数据安全" ]
  [ "殷尚男（1989- ），男，吉林白城人，博士，江苏大学副教授，主要研究方向为入侵检测及孤立点检测" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB1400700);江苏省前沿引领技术基础研究专项基金资助项目(BK20202001);国家自然科学基金资助项目(61802154);国家自然科学基金资助项目(61702230);国家自然科学基金资助项目(U1836116)
- DOI：10.11959/j.issn.1000-436x.2021030
  中图分类号： TP18
- 网络出版日期：2021-05，
  
  纸质出版日期：2021-05-25
- 稿件说明：
移动端阅览
朱会娟, 陈锦富, 李致远, 等. 基于多特征自适应融合的区块链异常交易检测方法[J]. 通信学报, 2021,42(5):41-50.

Huijuan ZHU, Jinfu CHEN, Zhiyuan LI, et al. Block-chain abnormal transaction detection method based on adaptive multi-feature fusion[J]. Journal on communications, 2021, 42(5): 41-50.
朱会娟, 陈锦富, 李致远, 等. 基于多特征自适应融合的区块链异常交易检测方法[J]. 通信学报, 2021,42(5):41-50. DOI： 10.11959/j.issn.1000-436x.2021030.

Huijuan ZHU, Jinfu CHEN, Zhiyuan LI, et al. Block-chain abnormal transaction detection method based on adaptive multi-feature fusion[J]. Journal on communications, 2021, 42(5): 41-50. DOI： 10.11959/j.issn.1000-436x.2021030.

摘要

针对智能检测模型的性能受限于原始数据（特征）表达能力的问题，设计了一种残差网络结构ResNet-32用于挖掘区块链交易特征间隐含的关联关系，自动学习包含丰富语义信息的高层抽象特征。虽然浅层特征区分能力弱，但更忠于原始交易细节的描述，如何充分利用两者的优势是提升异常交易检测性能的关键，因此提出了特征融合方法自适应地桥接高层抽象特征与原始特征之间的鸿沟，自动去除其噪声和冗余信息，并挖掘两者的交叉特征信息获得最具区分力的特征。最后，结合以上方法提出区块链异常交易检测模型（BATDet），并通过Elliptic数据集验证了所提模型在区块链异常交易检测领域的有效性。

Abstract

Aiming at the problem that the performance of intelligent detection models was limited by the representation ability of original data (features)

a residual network structure ResNet-32 was designed to automatically mine the intricate association relationship between original features

so as to actively learn the high-level abstract features with rich semantic information.Low-level features were more transaction content descriptive

although their distinguishing ability was weaker than that of the high-level features.How to integrate them together to obtain complementary advantages was the key to improve the detection performance.Therefore

multi feature fusion methods were proposed to bridge the gap between the two kinds of features.Moreover

these fusion methods can automatically remove the noise and redundant information from the integrated features and further absorb the cross information

to acquire the most distinctive features.Finally

block-chain abnormal transaction detection model (BATDet) was proposed based on the above presented methods

and its effectiveness in the abnormal transaction detection is verified.

关键词

Keywords

references

龚鸣 . 非基于 KYC 的区块链反洗钱技术探讨 [J ] . 清华金融评论 , 2017 , 4 : 52 - 54 .

GONG M . Discussion on anti money laundering technology of blockchain nor based on KYC [J ] . Tsinghua Financial Review , 2017 , 4 : 52 - 54 .

QI T T , WANG T M , ZHU J M , et al . Empirical study on the correlation and volatility between bitcoin and the blockchain index:based on granger causality test and GARCH-class model [C ] // The 4th International Conference on Crowd Science and Engineering . New York:ACM Press , 2019 : 28 - 32 .

WEBER M , DOMENICONI G , CHEN J , et al . Anti-money laundering in bitcoin:experimenting with graph convolutional networks for financial forensics [J ] . arXiv Preprint,arXiv:1908.02591 , 2019 .

钱卫宁 , 邵奇峰 , 朱燕超 , 等 . 区块链与可信数据管理:问题与方法 [J ] . 软件学报 , 2018 , 29 ( 1 ): 150 - 159 .

QIAN W N , SHAO Q F , ZHU Y C , et al . Research problems and methods in blockchain and trusted data management [J ] . Journal of Software , 2018 , 29 ( 1 ): 150 - 159 .

LECUN Y , BENGIO Y , HINTON G . Deep learning [J ] . Nature , 2015 , 521 ( 7553 ): 436 - 444 .

REN K , ZHENG T H , QIN Z , et al . Adversarial attacks and defenses in deep learning [J ] . Engineering , 2020 , 6 ( 3 ): 346 - 360 .

ZHAO Z Q , ZHENG P , XU S T , et al . Object detection with deep learning:a review [J ] . IEEE Transactions on Neural Networks and Learning Systems , 2019 , 30 ( 11 ): 3212 - 3232 .

Elliptic . Elliptic company [R ] .(2019)[2020-11-20 ] .

余春堂 , 韩志耕 , 李致远 , 等 . 基于区块链的众包物流分级多层智能服务交易监管架构 [J ] . 网络与信息安全学报 , 2020 , 6 ( 3 ): 50 - 58 .

YU C T , HAN Z G , LI Z Y , et al . Blockchain-based hierarchical and multi-level smart service transaction supervision framework for crowdsourcing logistics [J ] . Chinese Journal of Network and Information Security , 2020 , 6 ( 3 ): 50 - 58 .

JULLUM M , LLAND A , HUSEBY R B , et al . Detecting money laundering transactions with machine learning [J ] . Journal of Money Laundering Control , 2020 , 23 ( 1 ): 173 - 186 .

DEMETIS D S . Fighting money laundering with technology:a case study of Bank X in the UK [J ] . Decision Support Systems , 2018 , 105 : 96 - 107 .

PAULA E L , LADEIRA M , CARVALHO R N , et al . Deep learning anomaly detection as support fraud investigation in Brazilian exports and anti-money laundering [C ] // 2016 15th IEEE International Conference on Machine Learning and Applications . Piscataway:IEEE Press , 2016 : 954 - 960 .

CHEN Z Y , KHOA L D , TEOH E N , et al . Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection:a review [J ] . Knowledge and Information Systems , 2018 , 57 ( 2 ): 245 - 285 .

PHAM T , LEE S . Anomaly detection in the bitcoin system - a network perspective [J ] . arXiv Preprint,arXiv:1611.03942v2 , 2016 .

LORENZ J , SILVA M L , APARICIO D , et al . Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity [J ] . arXiv Preprint,arXiv:1611.03942v2 , 2016 .

李韵 , 黄辰林 , 王中锋 , 等 . 基于机器学习的软件漏洞挖掘方法综述 [J ] . 软件学报 , 2020 , 31 ( 7 ): 2040 - 2061 .

LI Y , HUANG C L , WANG Z F , et al . Survey of software vulnerability mining methods based on machine learning [J ] . Journal of Software , 2020 , 31 ( 7 ): 2040 - 2061 .

LI X , DING M L , PIŽURICA A . Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2020 , 58 ( 4 ): 2615 - 2629 .

王勇 , 周慧怡 , 俸皓 , 等 . 基于深度卷积神经网络的网络流量分类方法 [J ] . 通信学报 , 2018 , 39 ( 1 ): 14 - 23 .

WANG Y , ZHOU H Y , FENG H , et al . Network traffic classification method basing on CNN [J ] . Journal on Communications , 2018 , 39 ( 1 ): 14 - 23 .

刘烨 , 黄金筱 , 马于涛 . 基于混合神经网络和注意力机制的软件缺陷自动分派方法 [J ] . 计算机研究与发展 , 2020 , 57 ( 3 ): 461 - 473 .

LIU Y , HUANG J X , MA Y T . An automatic method using hybrid neural networks and attention mechanism for software bug triaging [J ] . Journal of Computer Research and Development , 2020 , 57 ( 3 ): 461 - 473 .

PANG J M , CHEN K , SHI J P , et al . Libra R-CNN:towards balanced learning for object detection [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 821 - 830 .

LIU S , HUANG D , WANG Y . Learning spatial fusion for single-shot object detection [J ] . arXiv Preprint,arXiv:1911.09516 , 2019 .

GHIASI G , LIN T Y , LE Q V . NAS-FPN:learning scalable feature pyramid architecture for object detection [C ] // 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2019 : 7029 - 7038 .

ZHANG Z , ZHANG X , PENG C , et al . ExFuse:enhancing feature fusion for semantic segmentation [C ] // European Conference on Computer Vision . Berlin:Springer , 2018 : 273 - 288 .

VINCENT P , LAROCHELLE H , LAJOIE I , et al . Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion [J ] . Journal of Machine Learning Research , 2010 , 11 ( 12 ): 3371 - 3408 .

LI K , ZOU C Q , BU S H , et al . Multi-modal feature fusion for geographic image annotation [J ] . Pattern Recognition , 2018 , 73 : 1 - 14 .

LI G H , MATTHIAS M , QIAN G C , et al . DeepGCNs:making GCNs go as deep as CNNs [J ] . arXiv Preprint,arXiv:1904.03751 , 2019 .

刘树东 , 王晓敏 , 张艳 . 一种对称残差 CNN 的图像超分辨率重建方法 [J ] . 西安电子科技大学学报 , 2019 , 46 ( 5 ): 15 - 23 .

LIU S D , WANG X M , ZHANG Y . Symmetric residual convolution neural networks for the image super-resolution reconstruction [J ] . Journal of Xidian University , 2019 , 46 ( 5 ): 15 - 23 .

HE K M , ZHANG X Y , REN S Q , et al . Deep residual learning for image recognition [C ] // 2016 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2016 : 770 - 778 .

裴伟 , 许晏铭 , 朱永英 , 等 . 改进的 SSD 航拍目标检测方法 [J ] . 软件学报 , 2019 , 30 ( 3 ): 738 - 758 .

PEI W , XU Y M , ZHU Y Y , et al . The target detection method of aerial photography images with improved SSD [J ] . Journal of Software , 2019 , 30 ( 3 ): 738 - 758 .

NAIR V , HINTON G E . Rectified linear units improve restricted Boltzmann machines Vinod Nair [C ] // IEEE International Conference on Computer Vision . Piscataway:IEEE Press , 2010 : 807 - 814 .

CHEN Y S , JIANG H L , LI C Y , et al . Deep feature extraction and classification of hyper spectral images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing , 2016 , 54 ( 10 ): 6232 - 6251 .

IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift [J ] . arXiv Preprint,arXiv:1502.03167 , 2015 .

KINGMAD , BA J . Adam:a method for stochastic optimization [C ] // International Conference on Learning Representations . Piscataway:IEEE Press , 2015 : 1 - 11 .

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