Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18470%2F22%3A50019185" target="_blank" >RIV/62690094:18470/22:50019185 - isvavai.cz</a>

Result on the web

<a href="https://peerj.com/articles/cs-956/" target="_blank" >https://peerj.com/articles/cs-956/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.7717/peerj-cs.956" target="_blank" >10.7717/peerj-cs.956</a>

Result language

angličtina

Original language name

Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification

Original language description

The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

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

Name of the periodical

PeerJ Computer Science

ISSN

2376-5992

e-ISSN

2376-5992

Volume of the periodical

8

Issue of the periodical within the volume

April

Country of publishing house

GB - UNITED KINGDOM

Number of pages

38

Pages from-to

"Article Number: e956"

UT code for WoS article

000795493600003

EID of the result in the Scopus database

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