Illicit data resale detection method via an enhanced relational graph convolutional network

Wang Yuxiang; Zhang Lingcui; Hou Yuqiao; Yang Qian; Niu Ben

doi:10.11959/j.issn.1000-436x.2026083

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Illicit data resale detection method via an enhanced relational graph convolutional network

Papers | 更新时间：2026-05-07

- Illicit data resale detection method via an enhanced relational graph convolutional network
- Journal on Communications Vol. 47, Issue 4, Pages: 113-125(2026)
- 作者机构：
  
  1.中国科学院信息工程研究所，北京 100085
  2.中国科学院大学网络空间安全学院，北京 100049
  3.网络空间安全防御全国重点实验室，北京 100085
- 作者简介：
- 基金信息：
  
  The National Key Research and Development Program of China(2023YFB3106505);The National Natural Science Foundation of China(U24A20240;62441226)
- DOI：10.11959/j.issn.1000-436x.2026083
  CLC： TN92
- Received：02 February 2026，
  
  Revised：2026-03-22，
  
  Accepted：26 March 2026，
  
  Published：20 April 2026
- 稿件说明：
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王宇翔,张玲翠,侯雨桥等.基于增强关系图卷积网络的数据违规转售检测方法[J].通信学报,2026,47(04):113-125.

Wang Yuxiang,Zhang Lingcui,Hou Yuqiao,et al.Illicit data resale detection method via an enhanced relational graph convolutional network[J].Journal on Communications,2026,47(04):113-125.
王宇翔,张玲翠,侯雨桥等.基于增强关系图卷积网络的数据违规转售检测方法[J].通信学报,2026,47(04):113-125. DOI： 10.11959/j.issn.1000-436x.2026083.

Wang Yuxiang,Zhang Lingcui,Hou Yuqiao,et al.Illicit data resale detection method via an enhanced relational graph convolutional network[J].Journal on Communications,2026,47(04):113-125. DOI： 10.11959/j.issn.1000-436x.2026083.

摘要

为解决数据流通交易场景下的数据违规转售问题，基于交易上下文信息相似度和交易因果顺序约束，对关系图卷积网络的消息传递和特征聚合过程进行改进，提出一种增强关系图卷积网络模型，可有效学习复杂交易关系下的违规转售行为特征。基于该模型设计一种数据违规转售检测方法，预测数据交易拓扑图中节点是否为违规转售交易节点。构造带有违规转售样本的模拟数据交易数据集并展开对比实验，结果证明了所提方法的有效性。

Abstract

Illicit data resale in data trading scenarios exhibited strong concealment and was difficult to detect. An enhanced relational graph convolutional network was proposed by optimizing message passing and feature aggregation with transaction contextual similarity and causal temporal order constraints

enabling effective representation of illicit resale behaviors under complex transaction relations. Based on this model

a detection method was developed to predict the existence of illicit resale behaviors in transaction topology graphs. A simulated data trading dataset containing anomalous resale samples was constructed

and comparative experiments were performed. The results indicate that the proposed method provides an effective solution for illicit data resale detection in data trading scenarios.

关键词

Keywords

references

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