Block-chain abnormal transaction detection method based on adaptive multi-feature fusion

Huijuan ZHU; Jinfu CHEN; Zhiyuan LI; Shangnan YIN

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

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Block-chain abnormal transaction detection method based on adaptive multi-feature fusion

Papers | 更新时间：2024-06-06

- Block-chain abnormal transaction detection method based on adaptive multi-feature fusion
- Journal on Communications Vol. 42, Issue 5, Pages: 41-50(2021)
- 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：
- 基金信息：
  
  The National Key R＆D Program of China(2017YFB1400700);The Leading-edge Technology Program of Jiangsu Natural Science Foundation(BK20202001);The National Natural Science Foundation of China(61802154);The National Natural Science Foundation of China(61702230);The National Natural Science Foundation of China(U1836116)
- DOI：10.11959/j.issn.1000-436x.2021030
  CLC： TP18
- Online First：2021-05，
  
  Published：25 May 2021
- 稿件说明：
移动端阅览
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：

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

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