Data-driven network layer security detection model and simulation for the Internet of Things based on an artificial immune system

Abstract

Traditional network security detection models are trained offline using attack samples of known types. Although such models have high detection rates for known attack types, they cannot identify new attack types in the network layer. At present, these detection systems have the disadvantages of slow system construction and a high cost of model updating. Facing the increasing expansion of networks and endless attacks, these detection systems lack self-adaptability and expansibility, so it is difficult to detect complex and changeable attack events in networks. In this paper, the integrated use of immunology theory, complex adaptive system theory, and computational experiment technology is proposed to develop an Internet network layer security detection model based on an artificial immune system as an improvement over existing models of Internet network layer security. On the basis of testing knowledge, when a new type of attack is encountered, the online detection and learning process enables the dynamic extension of the network security detection model. Experimental calculations and an example analysis are presented to verify the scientific validity and feasibility of the model.

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

Fig. 1
Fig. 2
Fig. 3

References

  1. 1.

    Zhibin Y, Lei P (2010) Architecture design and analysis language for complex embedded real-time system. J Softw 21(5):899–915

    Article  Google Scholar 

  2. 2.

    Gen Y, Jie X (2010) Internet of things security features and key technologies. J Nanjing Univ Posts Telecommun 30(4):20–29

    Google Scholar 

  3. 3.

    Qiang L, Li C (2010) Key technologies and applications of Internet of things. Comput Sci 37(6):1–4

    Google Scholar 

  4. 4.

    Can Ö (2017) On-shelf product detection: post-processing with a hidden Markov model. IEEE 7(3):1304–1313

    MathSciNet  Google Scholar 

  5. 5.

    Agah A, Basu K (2004) Intrusion detection in sensor networks: a non-copporative game approach. IEEE Int Symp Netw Comput Appl 12(7):1120–1129

    Google Scholar 

  6. 6.

    Greensmith J, Aickelin U, Tedesco G (2012) Information fusion for anomaly detection with the dendritic cell algorithm. Inf Fusion 11(1):21–34

    Article  Google Scholar 

  7. 7.

    Feng X, Qi L, Pan J (2017) A novel fault location method and algorithm for DC distribution protection. IEEE Trans Ind Appl 53(3):1834–1840

    Article  Google Scholar 

  8. 8.

    Yahui Y, Haizhen H (2014) Research on intrusion detection based on incremental GHSOM. Chin J Comput 37(5):1216–1223

    Google Scholar 

  9. 9.

    Hong M, Gang H (2014) A method of software architecture modeling in the whole lifecycle, Science in Chinese. Inf Sci 44(5):564–587

    Google Scholar 

  10. 10.

    Honggui H, Lidan W (2014) Hierarchical extreme learning machine for feedforward neural network. Neurocomputing 128(27):128–135

    Google Scholar 

  11. 11.

    Hualing S, Guofeng W (2012) Structure complexity assessment for safety system based on complex information metric. J Manag Sci China 15(2):83–96

    Google Scholar 

  12. 12.

    Haiming C, Li C (2016) Design and model checking of service oriented software architecture for internet of thing: a survey. Chin J Comput 39(5):853–871

    MathSciNet  Google Scholar 

  13. 13.

    Zhen L, Ban W (2012) Environmental emergency decision support system based on artificial neural network. Saf Sci 20(1):150–163

    Google Scholar 

  14. 14.

    Satpathi K, Yeap YM, Ukil A, Geddada N (2018) Short-time Fourier transform based transient analysis of VSC interfaced point-to-point DC system. IEEE Trans Ind Electron 65(5):4080–4091

    Article  Google Scholar 

  15. 15.

    Yongguang Z, Zhonggen M (2012) Progress of study on unconventional emergencies management’. Syst Eng Theory Pract 32(5):911–918

    Google Scholar 

  16. 16.

    Furao S, Hui Y (2011) An incremental online semi-supervised active learning algorithm based on a self-organizing incremental neural network. Neural Comput Softw Appl 20(1):1061–1094

    Google Scholar 

Download references

Acknowledgement

This work was financially supported by Jiangxi Provincial Department of Education Science and Technology Research Key Project (Grant No: GJJ180249); Social Science Planning Project of Jiangxi Province(19TQ01); and Jiangxi University Humanities and Social Science Research Project (GL18103).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Meifang Yang.

Ethics declarations

Conflict of interest

No conflicts of interest.

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

Verify currency and authenticity via CrossMark

Cite this article

Yang, B., Yang, M. Data-driven network layer security detection model and simulation for the Internet of Things based on an artificial immune system. Neural Comput & Applic (2020). https://doi.org/10.1007/s00521-020-05049-5

Download citation

Keywords

  • Internet-of-Things security
  • Artificial immune system
  • Computational experiments