Computer Network Information Security in the Big Data Era

  • Yanli LiuEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1146)


With the continuous development of globalization, the Internet technology has achieved unprecedented rapid development. With the increasing number of intelligent network terminals, technologies such as the Internet of Things and cloud computing have been continuously applied in various fields, making the amount of Internet data appear explosive. Upgrading the Internet has gradually entered the era of big data. In order to ensure the security of data information, it is of great significance to the problems of network information security (NIS) and security development in many fields. This article details the background in the era of big data. This article clarifies the urgency and necessity of NIS control in the context of big data. It proposes three elements of the NIS control mechanism, namely network control personnel, environment and technology. The three elements are the starting point to establish a NIS control mechanism. This article starts with related control strategies and security control technologies to establish a NIS control evaluation system. The NIS control evaluation system is used to provide NIS control work. Reference standard. This paper proposes the relevant strategies for NIS control, and provides necessary suggestions for NIS work. The research results show that the research results of this paper provide help and guidance for the development of NIS work in the context of big data.


Cloud computing Control-mechanism Informatization Control mechanism 


  1. 1.
    Vijayakumar Bharathi, S.: Prioritizing and ranking the big data information security risk spectrum. Glob. J. Flex. Syst. Manag. 18(2), 183–201 (2017)CrossRefGoogle Scholar
  2. 2.
    Dong, X.-M.: Learning gradients from nonidentical data. Anziam J. 58(3), 1–11 (2017)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Wang, X., Williams, C., Liu, Z.H.: Big data management challenges in health research. Brief. Bioinform. 20(1), 1–12 (2017)Google Scholar
  4. 4.
    Aditham, S., Ranganathan, N.: A system architecture for the detection of insider attacks in big data systems. IEEE Trans. Dependable Secure Comput. 15(6), 974–987 (2018)CrossRefGoogle Scholar
  5. 5.
    Luyao, F., Jing, Z., Chen, X.: Open source big data framework in marine information processing. Sci. Technol. Rev. 35(20), 126–133 (2017)Google Scholar
  6. 6.
    Chaudhary, R., Aujla, G.S., Kumar, N.: Optimized big data management across multi-cloud data centers: software-defined-network-based analysis. IEEE Commun. Mag. 56(2), 118–126 (2018)CrossRefGoogle Scholar
  7. 7.
    Moro Visconti, R.: Public private partnerships, big data networks and mitigation of information asymmetries. Corp. Ownersh. Control 14(4), 270 (2017)Google Scholar
  8. 8.
    Foley, S.N., Rooney, V: A grounded theory approach to security policy elicitation. Inf. Comput. Secur. 26(12) (2018)CrossRefGoogle Scholar
  9. 9.
    Al-Dhafian, B., Ahmad, I., Hussain, M.: Improving the security in healthcare information system through Elman neural network based classifier. J. Med. Imaging Health Inform. 7(6), 1429–1435 (2017)CrossRefGoogle Scholar
  10. 10.
    Garg, S., Peddoju, S.K., Sarje, A.K.: Network-based detection of Android malicious apps. Int. J. Inf. Secur. 16(4), 385–400 (2017)CrossRefGoogle Scholar
  11. 11.
    Hu, H., Wang, Z., Cheng, G.: MNOS: a mimic network operating system for software defined networks. IET Inf. Secur. 11(6), 345–355 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Network Information CenterBin Zhou Medical UniversityYantaiChina

Personalised recommendations