Blockchain-Based Covert Communication: A Detection Attack and Efficient Improvement

Zhuo Chen, Liehuang Zhu, Peng Jiang*, Zijian Zhang, Chengxiang Si

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Covert channels in blockchain networks achieve undetectable and reliable communication, while transactions incorporating secret data are perpetually stored on the chain, thereby leaving the secret data continuously susceptible to extraction. MTMM (IEEE Transactions on Computers 2023) is a state-of-the-art blockchain-based covert channel. It utilizes Bitcoin network traffic that will not be recorded on the chain to embed data, thus mitigating the above issues. However, we identify a distinctive pattern in MTMM, based on which we propose a comparison attack to accurately detect MTMM traffic. To defend against the attack, we present an improvement named ORIM, which exploits the permutation of transaction hashes within inventory messages to transmit secret data. ORIM leverages a pseudo-random function to obscure the transaction hashes involved in the permutation to ensure unobservability. The obfuscated values, rather than the original transaction hashes, are utilized to encode the confidential data. Furthermore, we introduce a variable-length encoding scheme predicated on complete binary trees. This scheme considerably amplifies the bandwidth and facilitates efficient encoding and decoding of secret data. Experimental results indicate that ORIM maintains unobservability and that ORIM's bandwidth is approximately 3.7× of MTMM.

Original languageEnglish
Pages (from-to)9698-9713
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume19
DOIs
Publication statusPublished - 2024

Keywords

  • Bitcoin
  • Blockchain
  • covert channel
  • covert communication
  • inventory message

Fingerprint

Dive into the research topics of 'Blockchain-Based Covert Communication: A Detection Attack and Efficient Improvement'. Together they form a unique fingerprint.

Cite this