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Analysis of Computer Network Information Security in the Era of Big Data

  • Zhenxing BianEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1117)

Abstract

In the era of big data, computer network has brought great convenience to people’s work and life, but also brought security risks in information. Therefore, the analysis of network information security plays an important role in the development and use of computer network. Under the background of big data, this paper puts forward three research hypotheses from three different factors of technology, personnel and environment, and constructs the evaluation model of computer network information security. By using the structural equation with good adaptability to test the research hypothesis, it is found that the correlation coefficients of the research hypothesis have no significant difference, and the model hypothesis is all valid. In this paper, entropy method is also introduced, and an index weight model is proposed. Experiments show that the index weight model based on entropy method is reasonable.

Keywords

Big data Computer network Information security Entropy method 

References

  1. 1.
    Purohit, L., Kumar, S.: Web services in the Internet of Things and smart cities: a case study on classification techniques. IEEE Consum. Electron. Mag. 8(2), 39–43 (2019)CrossRefGoogle Scholar
  2. 2.
    Camacho, D.: Bio-inspired clustering: basic features and future trends in the era of Big Data. In: IEEE International Conference on Cybernetics (2015)Google Scholar
  3. 3.
    Xi, R.R., Yun, X.C., Zhang, Y.Z., Hao, Z.Y.: An improved quantitative evaluation method for network security. Chin. J. Comput. 38(4), 749–758 (2015)MathSciNetGoogle Scholar
  4. 4.
    Pawar, M.V., Anuradha, J.: Network security and types of attacks in network. Procedia Comput. Sci. 48, 503–506 (2015)CrossRefGoogle Scholar
  5. 5.
    Yan, F., Jian-Wen, Y., Lin, C.: Computer network security and technology research. In: 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, pp. 293–296. IEEE (2015)Google Scholar
  6. 6.
    Guo, J.C., Fan, D., Che, H.Y., Duan, Y.N., Wang, H.S., Zhang, D.W.: An approach to network security evaluation of computer network information system with triangular fuzzy information. J. Intell. Fuzzy Syst. 28(5), 2029–2035 (2015)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Saridakis, G., Benson, V., Ezingeard, J.N., Tennakoon, H.: Individual information security, user behaviour and cyber victimisation: an empirical study of social networking users. Technol. Forecast. Soc. Change 102, 320–330 (2016)CrossRefGoogle Scholar
  8. 8.
    Vorobiev, E.G., Petrenko, S.A., Kovaleva, I.V., Abrosimov, I.K.: Analysis of computer security incidents using fuzzy logic. In: 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 369–371. IEEE (2017)Google Scholar
  9. 9.
    Shameli-Sendi, A., Aghababaei-Barzegar, R., Cheriet, M.: Taxonomy of information security risk assessment (ISRA). Comput. Secur. 57, 14–30 (2016)CrossRefGoogle Scholar
  10. 10.
    Ahmad, A., Maynard, S.B., Shanks, G.: A case analysis of information systems and security incident responses. Int. J. Inf. Manag. 35(6), 717–723 (2015)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Shandong Polytechnic CollegeJiningChina

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