Skip to main content
SpringerLink
Log in

A Network Security Situation Assessment Method Based on Multi-attention Mechanism and HHO-ResNeXt

  • Conference paper
  • First Online:
Security and Privacy in Social Networks and Big Data (SocialSec 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1663))

Abstract

Because the traditional convolutional neural network (CNN) cannot obtain the importance of each channel and its receptive field is limited, it is difficult to deal with the increasingly complex network environment. Aiming at the shortcomings, this paper combines the Contextual Transformer (COT) block and the Efficient Channel Attention (ECA) module with ResNeXt and uses Harris Hawks Optimization (HHO) algorithm to choose the most suitable hyperparameters to improve the model performance. This model enables the rich contexts among neighbor keys to be fully exploited and can obtain the importance of each channel to improve the weight of the useful channel and suppress the less useful channel, effectively making up for the shortcomings of the traditional convolutional neural network. The experiments on a network security dataset show that the model is superior to other models in the network security situation assessment effect and its comprehensive performance in accuracy, precision and F-score.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Endsley, M.R.: Design and evaluation for situation awareness enhancement. In: Proceedings of the Human Factors Society Annual Meeting, vol. 32, pp. 97–101. SAGE Publications, Los Angeles (1988)

    Google Scholar 

  2. Bass, T., Gruber, D.: A glimpse into the future of id. Mag. USENIX & SAGE 24(4), 40–45 (1999)

    Google Scholar 

  3. Bass, T., Robichaux, R.: Defense-in-depth revisited: qualitative risk analysis methodology for complex network-centric operations. In: 2001 MILCOM Proceedings Communications for Network-Centric Operations: Creating the Information Force (Cat. No. 01CH37277), vol. 1, pp. 64–70. IEEE (2001)

    Google Scholar 

  4. Gong, J., Zang, X., Su, Q., et al.: Survey of network security situation awareness. J. Softw. 28(04), 1010–1026 (2017)

    Google Scholar 

  5. Li, Y., Yao, T., Pan, Y., et al.: Contextual transformer networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. (2022)

    Google Scholar 

  6. Qi, L., Wang, B., Wu, P., et al.: ECA-NET: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, 13–19 June 2020, pp. 11531–11539. IEEE (2020)

    Google Scholar 

  7. Xie, S., Girshick, R., Dollár, P., et al.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)

    Google Scholar 

  8. Heidari, A.A., Mirjalili, S., Faris, H., et al.: Harris Hawks optimization: algorithm and applications. Futur. Gener. Comput. Syst. 97, 849–872 (2019)

    Article  Google Scholar 

  9. Zheng, W.: Research on situation awareness of network security assessment based on Dempster-Shafer. In: MATEC Web of Conferences, vol. 309, p. 02004. EDP Sciences (2020)

    Google Scholar 

  10. Cheng, J., Qi, Z., Cheng, T.: Network security situation awareness based on RBF neural networks. J. Nanjing Univ. Posts Telecommun. (Nat. Sci.) 39(04), 88–95 (2019). https://doi.org/10.14132/j.cnki.1673-5439.2019.04.012

    Article  Google Scholar 

  11. Samuel, O.S.: Cyber situation awareness perception model for computer network. Int. J. Adv. Comput. Sci. Appl. 12(1), 1–6 (2021)

    Google Scholar 

  12. Zhang, R., Zhang, Y., Liu, J., et al.: Network security situation prediction method using improved convolution neural network. Comput. Eng. Appl. 55(6), 86–93 (2019)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  15. Moustafa, N.,Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Proceedings of the 2015 Military Communications and Information Systems Conference (MilCIS), pp. 1–6. IEEE, Canberra (2015)

    Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant (No.61672206), the Central Government Guides Local Science and Technology Development Fund Project Under Grant (No.216Z0701G), the Key Research and Development Program of Hebei Under Grant (No.20310701D).

Author information

Authors and Affiliations

  1. School of College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, 050024, China

    Dongmei Zhao, Guoqing Ji & Shuiguang Zeng

  2. Hebei Provincial Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, 050024, China

    Dongmei Zhao

  3. Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security, Hebei Normal University, Shijiazhuang, 050024, China

    Dongmei Zhao

Authors
  1. Dongmei Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guoqing Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuiguang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongmei Zhao .

Editor information

Editors and Affiliations

  1. Xidian University, Xi’an, China

    Xiaofeng Chen

  2. Fujian Normal University, Fuzhou, China

    Xinyi Huang

  3. Wrocław University of Science and Technology, Wrocław, Poland

    Mirosław Kutyłowski

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhao, D., Ji, G., Zeng, S. (2022). A Network Security Situation Assessment Method Based on Multi-attention Mechanism and HHO-ResNeXt. In: Chen, X., Huang, X., Kutyłowski, M. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2022. Communications in Computer and Information Science, vol 1663. Springer, Singapore. https://doi.org/10.1007/978-981-19-7242-3_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-7242-3_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7241-6

  • Online ISBN: 978-981-19-7242-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation