A Multi-Dimensional Covert Transaction Recognition Scheme for Blockchain

Covert communication was widely studied in recent years in terms of keeping the communication of entities on the Internet secret from the point of view of information security. Due to the anonymity of accounts and the publicness of the ledger, blockchain is a natural and ideal channel for helping users establish covert communication channels. Senders can embed secret messages into certain fields in transactions, and receivers can extract those messages from the transactions without attracting the attention of other users. However, to the best of our knowledge, most existing works have aimed at designing blockchain-based covert communication schemes. Few studies concentrated on the recognition of transactions used for covert communication. In this paper, we first analyze convolutional neural network (CNN)-based and attention-based covert transaction recognition schemes, and we explore the deep relationship between the appropriate extraction of features and the embedded fields of covert transactions. We further propose a multi-dimensional covert transaction recognition (M-CTR) scheme. It can simultaneously support both one-dimensional and two-dimensional feature extraction to recognize covert transactions. The experimental results show that the precision and recall of the M-CTR in recognizing covert transactions outperformed those of existing covert communication schemes.