Generative blockchain-based covert communication model based on Markov chain

Wei SHE; Xinpeng RONG; Wei LIU; Zhao TIAN

doi:10.11959/j.issn.1000-436x.2022194

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Generative blockchain-based covert communication model based on Markov chain

Papers | 更新时间：2024-06-05

- Generative blockchain-based covert communication model based on Markov chain
- Journal on Communications Vol. 43, Issue 10, Pages: 121-132(2022)
- 作者机构：
  
  1. 郑州大学网络空间安全学院，河南郑州 450001
  2. 河南省网络密码技术重点实验室，河南郑州 450001
  3. 郑州市区块链与数据智能重点实验室，河南郑州 450001
- 作者简介：
- 基金信息：
  
  Program for Science＆Technology Innovation Talents in Universities of Henan Province(21HASTIT031);Key R＆D and Promotion Project in Henan Province(212102310039);Key R＆D and Promotion Project in Henan Province(212102310554);Major Public Welfare Project of Henan Province(201300210300);Program for Henan Key Laboratory of Network Cryptography Technology(LNCT2022-A04)
- DOI：10.11959/j.issn.1000-436x.2022194
  CLC： TP309.2
- Online First：2022-10，
  
  Published：25 October 2022
- 稿件说明：
移动端阅览
Wei SHE, Xinpeng RONG, Wei LIU, et al. Generative blockchain-based covert communication model based on Markov chain[J]. Journal on Communications, 2022, 43(10): 121-132.
DOI：

Wei SHE, Xinpeng RONG, Wei LIU, et al. Generative blockchain-based covert communication model based on Markov chain[J]. Journal on Communications, 2022, 43(10): 121-132. DOI： 10.11959/j.issn.1000-436x.2022194.

摘要

摘要：为了解决目前区块链隐蔽通信中信道构建风险高、信息交叉、隐蔽性不足等问题，提出了一种基于马尔可夫链的生成式区块链隐蔽通信模型。首先，发送方使用文本数据集获取候选单词集并进行马尔可夫模型训练，获得转移概率矩阵，并生成哈夫曼树集合；随后，对需要传输的秘密信息二进制流进行迭代式哈夫曼解码，以获得一组符合正常语言与语义特征、可读性强的载密信息语句，利用生成式隐写方法完成秘密信息嵌入；然后，将该载密信息进行环签名后，作为正常交易发布到区块链网络中并完成打包和出块；最后，接收方利用相同的文本数据集获取转移概率权值哈夫曼树，逆向操作获得秘密信息二进制流。实验结果表明，相较于目前的同类模型，所提模型可进一步提高嵌入强度和时间效率，降低隐蔽信道构建风险，避免信息交叉，提升隐蔽性。

Abstract

To solve the problems of high channel construction risk

information crossover

and insufficient concealment in the blockchain covert communication

a generative blockchain-based covert communication model based on Markov chain was proposed.First

the text data set was used by sender to obtain the candidate words set and trained the Markov model to obtain the transition probability matrix

generated the Huffman tree set.Secret message to be transmitted was performed iterative Huffman decoding on the binary stream to obtain a set of highly readable carring-secret message statements that conformed to normal language and semantic characteristics

a generative steganography was used to complete secret message embedding.Then

the carring-secret message was ring-signed and published to the blockchain as a normal transaction packing and block generation were completed in the network.Finally

the same text data set was used by the receiver to obtain the Huffman tree of transition probability weights

the binary stream of secret message was obtained by reverse operation.Simulation results demonstrate that

compares with the current similar models

the proposed model can further improve the embedding strength and time efficiency

reduce the risk of covert channel construction

avoid information crossover

and improve the concealment.

关键词

Keywords

references

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