Skip to main content
SpringerLink
Log in

Application of Neuro-Fuzzy Inference to Detect Network Scanning

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract—

Neuro-fuzzy systems, the hybrid computational structures integrating elements of artificial networks and fuzzy logic, are reviewed. Modern neuro-fuzzy systems are investigated, a neuro-fuzzy toolkit for detecting network scanning using a neuro-fuzzy system is proposed, and efficiency analysis of the given technology is carried out. The applicability of hybrid neuro-fuzzy system combining a neural network of signal forward propagation and the basis of Takagi–Sugeno fuzzy inference are substantiated and confirmed.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

REFERENCES

  1. Mamdani, E.H. and Assilian, S., An experiment in linguistic synthesis with a fuzzy logic controller, Int. J. Man-Mach. Studies, 1975, vol. 7, no. 1, pp. 1–13.  https://doi.org/10.1016/S0020-7373(75)80002-2

    Article  MATH  Google Scholar 

  2. Tsukamoto, Y., An approach to fuzzy reasoning method, Adv. Fuzzy Set Theory Appl., 1979, pp. 137–149.

    Google Scholar 

  3. Takagi, T. and Sugeno, M., Fuzzy identification of systems and its applications to modeling and control, IEEE Trans. Syst., Man, Cybern., 1985, vol. 15, no. 1, pp. 116–132.  https://doi.org/10.1109/TSMC.1985.6313399

    Article  MATH  Google Scholar 

  4. Aik, L. and Jayakumar, Y., A study of neuro-fuzzy system in approximation-based problems, Mat.: Malays. J. Ind. Appl. Math., 2008, vol. 24, no. 2, pp. 113–130.  https://doi.org/10.11113/matematika.v24.n.534

    Article  Google Scholar 

  5. Jang, J.-S.R., ANFIS: adaptive-network-based fuzzy inference systems, IEEE Trans. Syst., Man, Cybern., 1993, vol. 23, no. 3, pp. 665–685.  https://doi.org/10.1109/21.256541

    Article  Google Scholar 

  6. Lin, C.-T. and Lee, C.S.G., Neural network based fuzzy logic control and decision system, IEEE Trans. Comput., 1991, vol. 40, no. 12, pp. 1320–1336.  https://doi.org/10.1109/12.106218

    Article  MathSciNet  MATH  Google Scholar 

  7. Vieira, J., Dias, F.M., and Mota, A., Neuro-fuzzy systems: a survey, WSEAS Trans. Syst., 2004, vol. 3, no. 2, pp. 414–419.

    Google Scholar 

  8. Juang, C.-F. and Lin, C.-T., An online self-constructing neural fuzzy inference network and its applications, IEEE Trans. Fuzzy Syst., 1998, vol. 6, no. 1, pp. 12–32.  https://doi.org/10.1109/91.660805

    Article  Google Scholar 

  9. Nürnberger, A., Nauck, D., and Kruse, R., Neuro-fuzzy control based on the NEFCON-model: recent developments, Soft Comput., 1998, vol. 2, no. 4, pp. 168–182.  https://doi.org/10.1007/s005000050050

    Article  Google Scholar 

  10. Kasabov, N.K. and Song, Q., DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction, IEEE Trans. Fuzzy Syst., 2002, vol. 10, no. 2, pp. 144–154.  https://doi.org/10.1109/91.995117

    Article  Google Scholar 

Download references

Funding

This study was supported by the Russian Foundation for Basic Research, scientific project no. 18-29-03102.

Author information

Authors and Affiliations

  1. Peter the Great St. Petersburg Polytechnic University, 195251, St. Petersburg, Russia

    M. O. Kalinin

Authors
  1. M. O. Kalinin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. O. Kalinin.

Ethics declarations

The author declares that he has no conflicts of interest.

Additional information

Translated by K. Gumerov

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kalinin, M.O. Application of Neuro-Fuzzy Inference to Detect Network Scanning. Aut. Control Comp. Sci. 55, 908–917 (2021). https://doi.org/10.3103/S0146411621080150

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411621080150

Keywords:

Search

Navigation