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A Hybrid Deep Learning Framework for Intrusion Detection in Database Systems Using Brown-Bear Optimization and Tunicate Swarm Algorithm

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Publicado en:Journal of Database Management vol. 36, no. 1 (2025), p. 1-26
Autor principal: Bansal, Shavi
Otros Autores: Attar, Razaz Waheeb, Alhomoud, Ahmed, Wang, Li, Gupta, Brij B., Gaurav, Akshat, Chen, Mu-Yen, Arya, Varsha
Publicado:
IGI Global
Materias:
Accuracy
Comparative studies
Algorithms
Datasets
Databases
Learning
Artificial intelligence
Deep learning
Neural networks
Optimization
Comparative analysis
Cybersecurity
Intrusion
Frame analysis
Loss reduction
Intrusion detection systems
Acceso en línea:Citation/Abstract
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Resumen:Protection of networks from changing cyberthreats depends critically on intrusion detection. This article presents a hybrid deep learning framework using a tunicate swarm algorithm and brown-bear optimization for intrusion detection. The Tunicate Swarm Algorithm (TSA) was utilized for hyperparameter tuning; the Brown-Bear Optimization Algorithm (BBOA) was employed for feature selection, therefore lowering the dataset from 41 to 25 features. After five epochs, the model tested on the NSL-KDD dataset achieves 98% accuracy. Comparative study using conventional models showed that the suggested framework improved accuracy and loss reduction, therefore stressing its possibilities to improve intrusion detection systems.
ISSN:1063-8016
1533-8010
1047-9430
DOI:10.4018/JDM.388847
Fuente:ABI/INFORM Global