Abstract
This paper presents a new method for steganography detection in network protocols. The method is based on a multilayer approach for the selective analysis of derived and aggregated metrics utilizing machine learning algorithms. The main objective is to provide steganalysis capability for networks with large numbers of devices and connections. We discuss considerations for performance analysis and present results. We also describe a means of applying our method for multilayer detection of a popular RSTEG (Retransmission Steganography) technique.
Highlights
Network steganography has recently gained considerable attention in the scientific community.Many new methods have been developed, and many more will be developed in the near future [1] as new network protocols are constantly being developed
As a part of the method, we have presented a steganalysis layer selection method that provides an intelligent selection of steganalysis algorithms, preserving the balance between resource consumption and detection performance
Based on the above findings, we suggest limiting the use of our method to stream modification and hybrid network steganography detection
Summary
Network steganography has recently gained considerable attention in the scientific community. This paper focuses solely on the detection of steganography techniques that operate at the network protocol level. In line with analyzed network traffic; In near real-time regimes. Some of the accurate detection methods tailored for specific network steganography techniques cannot be effectively implemented in real-time regimes because excessive computing and/or memory resources are needed [5]. This makes us question the overall accuracy of such methods since they are unable to analyze high-throughput traffic in a multi-host environment. Our motivation is to provide a generic method that orchestrates network steganography detection in real-time regime, making it possible to implement in multi-host environments that generate high-throughput traffic.
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