A CNN and RNN fusion model-based network heterogeneous concurrent steganography channel detection method

Abstract

The invention belongs to the field of information technology security, and particularly relates to a CNN and RNN fusion model-based network heterogeneous concurrent steganalysis channel detection method. The method comprises the following steps of: extracting detection features of the multi-dimensional concurrent covert channel based on deep learning; learning the extracted detection characteristics; constructing a multi-dimensional concurrent covert channel detector and carrying out detection; According to the method, the defects of large feature dimension and incapability of expressing multi-layer data complex association caused by artificial design features based on experience or heuristic knowledge are avoided, and the detection of multi-dimensional concurrent covert communication of the network streaming media is realized by utilizing an automatic representation method for automatically mining feature association based on deep learning.

Description

technical field [0001] The invention belongs to the field of information technology security, and in particular relates to a method for detecting network heterogeneous concurrent steganographic channels based on a fusion model of CNN and RNN. Background technique [0002] In recent years, with the continuous and rapid growth of the Internet, streaming media (Streaming Media) services on the Internet have achieved unprecedented development. At present, streaming media has a wide range of applications, such as: VOD (Video On Demand), AOD (Audio On Demand), IPTV (Internet Protocol Television), VoIP (Voice over IP) and so on. The network data generated by these network streaming media services has the characteristics of large data volume and complex structure, which makes network streaming media an excellent carrier for covert communication. Due to the instantaneous and real-time nature of streaming media, the information hiding technology in traditional static (storage) carrie...

AI Technical Summary

Problems solved by technology

Due to the use of different steganographic algorithms, it is difficult to obtain the correlation characteristics between concurrent channels through some traditional experience or observation

Due to the high cost of artificial feature construction and the high cost of feature selection, for massive network streaming media, the huge workload of feature engineering is a great constraint factor

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