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

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.

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Publication Details

Journal
Journal of Database Management
Published
2025-09-25
DOI
https://doi.org/10.4018/jdm.388847
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Razaz Waheeb Attar, Mu‐Yen Chen, Akshat Gaurav, Yan Li et al.
Journal of Database Management
Network Security and Intrusion Detection
article

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

Razaz Waheeb Attar, Mu‐Yen Chen, Akshat Gaurav, Yan Li, Brij B. Gupta, Varsha Arya, Shavi Bansal, Ahmed Alhomoud
article en

Abstract

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.

Journal of Database ManagementVol. 36(1)
Princess Nourah bint Abdulrahman University (SA), Northern Border University (SA), Asia University (TW), China Medical University (TW), China Medical University Hospital (TW), Shanghai University of Medicine and Health Sciences (CN), University of Business and Technology (SA), University of Petroleum and Energy Studies (IN), National Cheng Kung University (TW)
National Science and Technology Council, Program of Shanghai Academic Research Leader
Openalex Percentile: Top 70%
Network Security and Intrusion Detection
DOIOpen Access PDFOpenAlex
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A Hybrid Deep Learning Framework for Intrusion Detection in Database Systems Using Brown-Bear Optimization and Tunicate Swarm Algorithm — Razaz Waheeb Attar, Mu‐Yen Chen, et al. · Journal of Database Management (2025) | TGRS Research Map | TGRS