Skip to main content
SpringerLink
Log in

Overview of Network Security Situation Awareness Based on Big Data

  • Conference paper
  • First Online:
Advanced Machine Learning Technologies and Applications (AMLTA 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1339))

Abstract

With the rapid development of Internet technology, network security issues have become more complex and changeable. Situational awareness can dynamically reflect network security’s overall situation and predict the development trend of network security. The application of big data technology creates opportunities for breakthroughs in large-scale network security situational awareness research. This paper introduces the application of big data in network security situational awareness. By evaluating network security and perceiving anomalous events in the network, we can predict the future security situation and block anomalous feedback. Network security situational awareness based on big data can better deal with increasingly complex network security problems.

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 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
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. Tianfield, H.: Cyber security situational awareness. IEEE (2017)

    Google Scholar 

  2. Guilan, F., Zhengnan, L., Wengang, Z.: Research review of big data analysis technology in network field. Comput. Sci. 46(06), 1–20 (2019)

    Google Scholar 

  3. Fangfang, G., Luomeng, C., Jianwen, Z.: Parallel preprocessing method of multi-source data based on similar connection. Comput. Appl. 39(01), 57–60 (2019)

    Google Scholar 

  4. Fumei, C., Dezhi, H., Kun, B., et al.: Analysis of key technologies for distributed data stream processing in big data environment. Comput. Appl. 37(03), 620–627 (2017)

    Google Scholar 

  5. Gupta, D., Singhal, S., Malik, S., et al.: Network intrusion detection system using various data mining techniques. IEEE (2016)

    Google Scholar 

  6. Yin, C., Zhu, Y., Fei, J., et al.: A deep learning approach for intrusion detection using recurrent neural networks. IEEE Access 5, 21954–21961 (2017)

    Article  Google Scholar 

  7. Hongwei, D., Liang, W., Kang, Z., et al.: Intrusion detection based on deep convolution neural network. Comput. Sci. 6, 48231–48246 (2019)

    Google Scholar 

  8. Hai-He, T.: Intrusion detection method based on improved neural network. In: 2018 International Conference on Smart Grid and Electrical Automation (ICSGEA), pp. 151–154. IEEE (2018)

    Google Scholar 

  9. Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2016)

    Article  Google Scholar 

  10. Xiaofeng, Z., Xiaohong, H.: Research on intrusion detection based on improved combination of K-means and multi-level SVM. In: 2017 IEEE 17th International Conference on Communication Technology (ICCT), pp. 2042–2045. IEEE (2017)

    Google Scholar 

  11. Zixuan, F., Yang, X., Zhaodi, W., et al.: SVM based on incremental learning_KNN network intrusion detection method_ Fu Zixuan. Computer Engineering (2019)

    Google Scholar 

  12. Yang, T., Jia, S.: Research on network security visualization under big data environment. In: IEEE Computer Society. International Computer Symposium (2016)

    Google Scholar 

  13. Wu, C., Sheng, S., Dong, X.: Research on visualization systems for DDoS attack detection. In: 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (2018)

    Google Scholar 

  14. Romero-Gomez, R., Nadji, Y., Antonakakis, M.: Towards designing effective visualizations for DNS-based network threat analysis. IEEE (2017)

    Google Scholar 

  15. Yi, C., Yuangang, Z., Haiyun, H., et al.: A visualization method for multi-dimensional attributes in hierarchical structure. Acta software Sinica. 27(05), 1091–1102 (2016)

    Google Scholar 

  16. Quanmin, W., Xiaofang, H.: Visualization analysis of network security big data based on NetFlow. Comput. Syst. Appl. 28(04), 1–8 (2019)

    Google Scholar 

  17. Jia, Z., Wang, N., Wang, Y., et al.: The traceability analysis and research of Botnet control center based on ant colony group-dividing algorithm. IEEE (2018)

    Google Scholar 

  18. Frigault, M., Wang, L.: Measuring network security using bayesian network-based attack graphs. In: 2008 32nd Annual IEEE International Computer Software and Applications Conference, pp. 698–703. IEEE (2008)

    Google Scholar 

  19. Gang, Z., Jinjing, W., Yingbin, H.: Research and design of the method for dealing with university network security incidents based on firewall strategy. Netw. Secur. Technol. Appl. 12, 89–90 (2018)

    Google Scholar 

Download references

Funding

Outstanding young scientific research talents cultivation plan of Fujian Province in 2016 (gy-z160150), doctoral research start-up fund of Fujian Institute of Technology (gy-z15009).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Fujan University of Technology, Fuzhou, 350118, China

    Weimin Qian, Hongtu Lai, Quan Zhu & Kuo-Chi Chang

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujan University of Technology, Fuzhou, 350118, China

    Hongtu Lai, Quan Zhu & Kuo-Chi Chang

Authors
  1. Weimin Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hongtu Lai
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Quan Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kuo-Chi Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongtu Lai .

Editor information

Editors and Affiliations

  1. Information Technology Department, Cairo University, Computer and Information Faculty, Giza, Egypt

    Aboul-Ella Hassanien

  2. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, Fujian, China

    Kuo-Chi Chang

  3. International Center for Informatics Research, Beijing Jaiotong University, Beijing, China

    Tang Mincong

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qian, W., Lai, H., Zhu, Q., Chang, KC. (2021). Overview of Network Security Situation Awareness Based on Big Data. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_81

Download citation

Publish with us

Policies and ethics

Search

Navigation