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Network intrusion detection system using ANFIS classifier

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Abstract

As network security had really become a basic issue, there is been a lot of improvement in designs for past years. Among several designs, IDS is one of the greatest hits. It has all of the stores of being that the presence of inadequacy and the dubious considered the impedances make fuzzy developments sensible for such structures. Fuzzy plans are not regularly versatile and do not have the ability to make models solely subject to the goal structure's model data. So, this paper aims in using Adaptive Neuro-Fuzzy Inference System (ANFIS) as a classifier for classifying the networks as malicious categories (Probe, DoS, U2R, R2L) and normal on DARPA 1999 database and are evaluated with other models like Fuzzy GNP, HHO in which experimental results show that ANFIS show better performance because it joins the upsides of both ANN and fuzzy deduction frameworks including the capacity to catch the nonlinear design of interaction, variation ability, and fast learning limit.

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Data availability statement

Enquiries about data availability should be directed to the authors.

Abbreviations

IDS:

Intrusion detection system

ML:

Machine learning

DL:

Deep learning

HHO:

Harris hawks optimization

F-GNP:

Fuzzy genetic network programming

ANFIS:

Adaptive neuro fuzzy inference system

ANN:

Artificial neural network

MF:

Member function

FAR:

False alarm rate

FGCS:

Future generation computer system

DoS:

Denial of service

U2R:

User to remote

R2L:

Remote to local

DIDS:

Database intrusion detection system

RBAC:

Rule based access control

SVM:

Support vector machine

KNN:

K-nearest neighbor

HMM:

Hidden Markov model

RF:

Random forest

KMC:

K-mean clustering

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The authors did not receive financial support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, India

    P. J. Sajith & G. Nagarajan

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  1. P. J. Sajith
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  2. G. Nagarajan
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Correspondence to G. Nagarajan.

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Communicated by Meng Joo.

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Sajith, P.J., Nagarajan, G. Network intrusion detection system using ANFIS classifier. Soft Comput 27, 1629–1638 (2023). https://doi.org/10.1007/s00500-022-06854-x

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