Skip to main content
SpringerLink
Log in

Research on information steganography based on network data stream

  • S.I. : ATCI 2020
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

To protect the information from being intercepted by third parties during the network communication process, this paper proposes a new type of data steganography technology based on network data flow. Using the network protocol itself and the relationship between data packets in the entire network data stream to perform network data steganography, transfer hidden data, and perform secondary identity authentication. Different from the traditional steganography method, this method can encode the hidden data and send the interval value by embedding the data packet, thereby hiding and transmitting the hidden data. In this technology, the operation of hidden data does not affect the user's access request for real network data, and it can perform processes such as hidden data transfer and secondary authentication without the user being able to detect it. Through experimental verification and evaluation, our method improves the concealment of the steganographic channel, is not easy to attract attention and has no obvious statistical characteristics of the traffic, and can improve the concealment and robustness of the steganography technology based on network data streams.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Zielinska E, Mazurczyk W, Szczypiorski K (2012) Development trends in steganography [J]. https://arXiv.org/1202.5289

  2. Kahn D (1996) The history of steganography [C]. International Workshop on Information Hiding

  3. 2018 Steganography [EB/OL]. https://en.wikipedia.org/wiki/Steganography

  4. Cheddad A, Condell J, Curran K et al (2010) Digital image steganography: survey and analysis of current methods [J]. Signal Process 90(3):727–752

    Article  Google Scholar 

  5. Nosrati M, Karimi R, Hariri M (2012) Audio steganography: a survey on recent approaches [J]. World Appl Program 2(3):202–205

    Google Scholar 

  6. Balaji R, Naveen G (2011) Secure data transmission using video steganography[C]. In: IEEE International Conference on Electro/information Technology

  7. Duan Y, Lee VCS, Lam K et al (2019) A cross-layer design for data dissemination in vehicular ad hoc networks. Neural Comput Appl 31:2869–2887

    Article  Google Scholar 

  8. Vimal S, Kalaivani L, Kaliappan M et al (2020) Development of secured data transmission using machine learning-based discrete-time partially observed Markov model and energy optimization in cognitive radio networks. Neural Comput Appl 32:151–161

    Article  Google Scholar 

  9. Ooi KS, Kong CL, Goay CH et al (2020) Crosstalk modeling in high-speed transmission lines by multilayer perceptron neural networks. Neural Comput Appl 32:7311–7320

    Article  Google Scholar 

  10. Provos N, Honeyman P (2003) Hide and seek: an introduction to steganography [J]. IEEE Secur Priv 99(3):32–44

    Article  Google Scholar 

  11. Fridrich J, Goljan M, Du R (2001) Detecting lsb steganography in color, and gray-scale images [J]. IEEE Multimedia 8(4):22–28

    Article  Google Scholar 

  12. Zhou S, Tan B (2020) Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Appl Soft Comput 86:105778

    Article  Google Scholar 

  13. Zhou S, Ke M, Luo P (2019) Multi-camera transfer GAN for person re-identification. J Vis Commun Image Represent 59:393–400

    Article  Google Scholar 

  14. Sheng DH, Kin TU (2011) A novel video steganography based on non-uniform rectangular partition [C]. In: IEEE International Conference on Computational Science and Engineering

  15. Zheng Xu, Guo L, Liu Y (2019) Special issue on intelligent signal processing methods and applications for photonic networks communications. Photon Netw Commun 37(2):139–140

    Article  Google Scholar 

  16. Dong K, Kim HJ, Yu X, Feng X (2020) Reversible data hiding for binary images based on adaptive overlapping pattern. EURASIP J Inf Secur 11:1–13

    Google Scholar 

  17. Luo X, Chan EWW, Chang RKC (2008) Tcp covert timing channels: Design and detection [C]. In: IEEE International Conference on Dependable Systems and Networks with Ftcs and Dcc

  18. Wu CW (2008) Research on stealth detection technology of network protocol [D]. Nanjing University of Science and Technology, Nanjing

    Google Scholar 

  19. Mingji Yu, Yuchen Liu Hu, Sun HY, Qiao T (2020) Adaptive and separable multiary reversible data hiding in encryption domain. EURASIP J Image Video Process 2020(1):16

    Article  Google Scholar 

  20. Kim H-W, Mu H, Park JH, Sangaiah AK, Jeong Y-S (2020) Video transcoding scheme of multimedia data-hiding for multiform resources based on intra-cloud. J Ambient Intell Humaniz Comput 11(5):1809–1819

    Article  Google Scholar 

  21. Anushiadevi R, Pravinkumar P, Rayappan JBB, Amirtharajan R (2020) A high payload separable reversible data hiding in cipher image with good decipher image quality. J Intell Fuzzy Syst 38(5):6403–6414

    Article  Google Scholar 

  22. Bazyar M, Sudirman R (2014) A recen treview of mp3 based steganography methods [J]. Intern J Secur Its Appl 8(6):405–414

    Google Scholar 

  23. Binny A, Koilakuntla M (2014) Hiding secret information using lsb based audio steganography [C]. In: 2014 International Conferenceon Soft Computing and Machine Intelligence

  24. Xiaolong Xu, Liu X, Zhanyang Xu, Dai F, Zhang X, Qi L (2019) Trust-oriented IoT Service placement for smart cities in edge computing. IEEE Internet Things J. https://doi.org/10.1109/JIOT.2019.2959124

    Article  Google Scholar 

  25. Meng T, Wolter K, Wu H, Wang Q (2018) A secure and cost-efficient offloading policy for mobile cloud computing against timing attacks. Pervasive Mob Comput 45:4–18

    Article  Google Scholar 

  26. Wu H, Han Z, Wolter K, Zhao Y, Ko H (2019) Deep learning driven wireless communications and mobile computing. Wirel Commun Mob Comput. https://doi.org/10.1155/2019/4578685

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Key R&D Program of China under Grant (No. 2017YFB0802300), the Key Research and Development Project of Sichuan Province (Nos. 20ZDYF2324, 2019ZYD027, 20ZDYF0660, 2018GZ0204), Sichuan Province Science and Technology Support Program (No. 2019JDRC0069), Director of Computer Application Research Institute Foundation SJ2020A08. Secondly, thanks to Center for Cyber Security Laboratory, UESTC, that provided us with an experiment.

Author information

Authors and Affiliations

  1. School of Cybersecurity, Chengdu University of Information Technology, Chengdu, 610225, Sichuan, China

    Jiazhong Lu, Shibin Zhang & Yan Chang

  2. School of Foreign Languages, Center for Digitalized Culture and Media, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Weisha Zhang

  3. Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu, 611731, Sichuan, China

    Ziye Deng

  4. Institute of Computer Application, China Academy of Engineering Physics, Mianyang, 621900, Sichuan, China

    Xiaolei Liu

Authors
  1. Jiazhong Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weisha Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ziye Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shibin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaolei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiaolei Liu.

Ethics declarations

Conflict of interest

We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, J., Zhang, W., Deng, Z. et al. Research on information steganography based on network data stream. Neural Comput & Applic 33, 851–866 (2021). https://doi.org/10.1007/s00521-020-05260-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05260-4

Keywords

Search

Navigation