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

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 s...

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Vydáno v:PeerJ. Computer science Ročník 8; s. e956
Hlavní autoři: Zivkovic, Miodrag, Tair, Milan, K, Venkatachalam, Bacanin, Nebojsa, Hubálovský, Štěpán, Trojovský, Pavel
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States PeerJ. Ltd 29.04.2022
PeerJ, Inc
PeerJ Inc
Témata:
Algorithms
Algorithms and Analysis of Algorithms
Artificial Intelligence
Autonomous Systems
Benchmark
Classification
Classifiers
Computational Linguistics
Data Mining and Machine Learning
Datasets
Detectors
Firefly algorithm
Heuristic methods
Intrusion detection
Intrusion detection systems
Machine learning
Mathematical functions
Neural networks
Optimisation
Optimization
Parameters
Security software
Tuning
Germany
Benchmark
Optimisation
Firefly algorithm
Intrusion detection
Machine learning
ISSN:2376-5992, 2376-5992
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Shrnutí: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.
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ISSN:2376-5992
2376-5992
DOI:10.7717/peerj-cs.956