A New Approach for Network Steganography Detection based on Deep Learning Techniques

One of the techniques that current cyber-attack methods often use to steal and transmit data out is to hide secret data in packets. This is the network steganography technique. Because millions of packets are sent and received every hour in internet activity, so it is very difficult to detect the theft and transmission of system data out using this form. Recent approaches often seek ways to compute and extract abnormal behaviors of packets to detect a steganography protocol or technique. However, such methods have the difficult problem of not being able to detect abnormal packets when an attacker uses other steganography techniques. To solve the above problem, this paper proposes a network steganography detection method using deep learning techniques. The highlight of this study is some new proposed features based on different components of the packet. By combining these many components, this proposal will not only provide the ability to detect many steganography techniques in the network, but also improve the ability to accurately detect abnormal packets. Besides, this study proposes to use deep learning for the task of detecting normal and abnormal packets. The authors want to take advantage of the big data analysis and processing capabilities of deep learning models in order to improve the ability to analyze and detect network steganography techniques. The experimental results in Section IVD have proved the effectiveness of this proposed method compared with other approaches.

@article{Xuan2021,
  title     = {A New Approach for Network Steganography Detection based on Deep Learning Techniques},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {7},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Cho Do Xuan and Lai Van Duong},
  doi       = {10.14569/IJACSA.2021.0120705},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120705}
}