A Blockchain-Enabled Group Covert Channel against Transaction Forgery

Tongzhou Shen, Liehuang Zhu, Feng Gao, Zhuo Chen, Zijian Zhang () and Meng Li ()
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Tongzhou Shen: School of Cyberspace Science and Technology, Beijing Institude of Technology, Beijing 100081, China
Liehuang Zhu: School of Cyberspace Science and Technology, Beijing Institude of Technology, Beijing 100081, China
Feng Gao: School of Cyberspace Science and Technology, Beijing Institude of Technology, Beijing 100081, China
Zhuo Chen: School of Cyberspace Science and Technology, Beijing Institude of Technology, Beijing 100081, China
Zijian Zhang: School of Cyberspace Science and Technology, Beijing Institude of Technology, Beijing 100081, China
Meng Li: School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China

Mathematics, 2024, vol. 12, issue 2, 1-24

Abstract: As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender’s identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender’s identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value).

Keywords: blockchain network; steganography; covert communication; covert channel (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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