RETRATCED ARTICLE: Network intrusion detection system using ANFIS classifier

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 developme...

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Vydáno v:Soft computing (Berlin, Germany) Ročník 27; číslo 3; s. 1629 - 1638
Hlavní autoři: Sajith, P. J., Nagarajan, G.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2023
Springer Nature B.V
Témata:
Adaptive systems
Artificial Intelligence
Artificial neural networks
Classification
Computational Intelligence
Control
Data mining
Datasets
Engineering
Focus
Fuzzy logic
Intrusion detection systems
Mathematical Logic and Foundations
Mechatronics
Robotics
Artificial neural network
Adaptive neuro-fuzzy inference system
Intrusion detection system
Genetic network programming
ISSN:1432-7643, 1433-7479
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Shrnutí: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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ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-022-06854-x