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Graph-Based Covert Transaction Detection and Protection in Blockchain.

Authors :
Guo, Zhenyu
Li, Xin
Liu, Jiamou
Zhang, Zijian
Li, Meng
Hu, Jingjing
Zhu, Liehuang
Source :
IEEE Transactions on Information Forensics & Security; 2024, Vol. 19, p2244-2257, 14p
Publication Year :
2024

Abstract

Covert communication is an method that plays an important role in secure data transmission. The technology embeds covert information into data and propagates it through covert channels. The communication quality depends on the choice of channel and data embedding techniques. Recently, blockchain has emerged to become the preferred channel to carry out covert communication for its decentralization and anonymity features. Existing covert transaction methods are constructed transaction-by-transaction, which makes them immune to text analysis-based detection methods. However, it is easy to expose their features on the transaction graph level. Unfortunately, there is yet no method to detect covert transactions by the features of transaction graph. In this paper, we propose a covert transaction detection method based on graph structure. By analyzing the statistical features of graph structure for addresses, we can infer whether they are the participants of covert transactions. Furthermore, we design a protection method of covert transactions based on graph generation networks. By adjusting the structural features between different addresses, our method enhances the security of multiple interrelated covert transactions. Experimental analysis on the Bitcoin Testnet verifies the security and the efficiency of the proposed methods. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15566013
Volume :
19
Database :
Complementary Index
Journal :
IEEE Transactions on Information Forensics & Security
Publication Type :
Academic Journal
Accession number :
174717885
Full Text :
https://doi.org/10.1109/TIFS.2023.3347895