Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50018604" target="_blank" >RIV/62690094:18470/22:50018604 - isvavai.cz</a>

Result on the web

<a href="https://www.techscience.com/cmc/v70n3/44980" target="_blank" >https://www.techscience.com/cmc/v70n3/44980</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.32604/cmc.2022.020769" target="_blank" >10.32604/cmc.2022.020769</a>

Result language

angličtina

Original language name

Improved Dragonfly Optimizer for Intrusion Detection Using Deep Clustering CNN-PSO Classifier

Original language description

With the rapid growth of internet based services and the data generated on these services are attracted by the attackers to intrude the networking services and information. Based on the characteristics of these intruders, many researchers attempted to aim to detect the intrusion with the help of automating process. Since, the large volume of data is generated and transferred through network, the security and performance are remained an issue. IDS (Intrusion Detection System) was developed to detect and prevent the intruders and secure the network systems. The performance and loss are still an issue because of the features space grows while detecting the intruders. In this paper, deep clustering based CNN have been used to detect the intruders with the help of Meta heuristic algorithms for feature selection and preprocessing. The proposed system includes three phases such as preprocessing, feature selection and classification. In the first phase, KDD dataset is preprocessed by using Binning normalization and Eigen-PCA based discretization method. In second phase, feature selection is performed by using Information Gain based Dragonfly Optimizer (IGDFO). Finally, Deep clustering based Convolutional Neural Network (CCNN) classifier optimized with Particle Swarm Optimization (PSO) identifies intrusion attacks efficiently. The clustering loss and network loss can be reduced with the optimization algorithm. We evaluate the proposed IDS model with the NSL-KDD dataset in terms of evaluation metrics. The experimental results show that proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall, f-measure and false detection rate.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

CMC-Computers, Materials &amp; Continua

ISSN

1546-2218

e-ISSN

1546-2226

Volume of the periodical

70

Issue of the periodical within the volume

3

Country of publishing house

US - UNITED STATES

Number of pages

17

Pages from-to

5949-5965

UT code for WoS article

000707364500018

EID of the result in the Scopus database

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