Feature selection in intrusion detection systems: a new hybrid fusion of Bat algorithm and Residue Number System

Autor: Yakub Kayode Saheed, Temitope Olubanjo Kehinde, Mustafa Ayobami Raji, Usman Ahmad Baba
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Information and Telecommunication, Vol 8, Iss 2, Pp 189-207 (2024)
Druh dokumentu: article
ISSN: 24751839
2475-1847
2475-1839
DOI: 10.1080/24751839.2023.2272484
Popis: ABSTRACTThis research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness.
Databáze: Directory of Open Access Journals
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