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

ABSTRACT: 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

SUBMITTER: Zivkovic M 

PROVIDER: S-EPMC9137854 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature