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

Yan Li et al.

Journal of Database Management2025https://doi.org/10.4018/jdm.388847article
AJG 1ABDC A
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0.50

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.

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https://doi.org/https://doi.org/10.4018/jdm.388847

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@article{yan2025,
  title        = {{A Hybrid Deep Learning Framework for Intrusion Detection in Database Systems Using Brown-Bear Optimization and Tunicate Swarm Algorithm}},
  author       = {Yan Li et al.},
  journal      = {Journal of Database Management},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.4018/jdm.388847},
}

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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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F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
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R · text relevance †0.50 × 0.4 = 0.20

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