A Hybrid Deep Learning Framework for Intrusion Detection in Database Systems Using Brown-Bear Optimization and Tunicate Swarm Algorithm
Year: 2025
Type: article
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.
Source: Journal of Database Management
DOI: 10.4018/jdm.388847
Institutions: Shanghai University of Medicine and Health Sciences, Asia University, China Medical University, Korea University, Symbiosis International University, China Medical University Hospital, National Cheng Kung University, Princess Nourah bint Abdulrahman University, Chandigarh University, University of Petroleum and Energy Studies, Hong Kong Metropolitan University, Northern Border University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G6501891654
OpenAlex ID: W4414508115