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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.
Details
Subject
Business indexing term
Title
A Hybrid Deep Learning Framework for Intrusion Detection in Database Systems Using Brown-Bear Optimization and Tunicate Swarm Algorithm
Author
Bansal, Shavi 1 ; Attar, Razaz Waheeb 2 ; Alhomoud, Ahmed 3 ; Wang, Li 4 ; Gupta, Brij B. 5 ; Gaurav, Akshat 6 ; Chen, Mu-Yen 7 ; Arya, Varsha 8
1 Insights2Techinfo, India
2 Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia
4 Shanghai University of Medicine and Health Sciences, Shanghai, China
5 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan & Symbiosis Centre for Information Technology, Symbiosis International University, Pune, India & School of Cybersecurity, Korea University, Seoul, South Korea
6 Ronin Institute, USA & International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan
7 Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
8 Hong Kong Metropolitan University, Hong Kong & UCRD, Chandigarh University, Chandigarh, India & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India
1 Insights2Techinfo, India
2 Management Department, College of Business Administration, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
3 Department of Computer Science, College of Science, Northern Border University, Arar, Saudi Arabia
4 Shanghai University of Medicine and Health Sciences, Shanghai, China
5 Department of Computer Science and Information Engineering, Asia University, Taichung, Taiwan & Department of Medical Research, China Medical University Hospital, China Medical University, Taichung, Taiwan & Symbiosis Centre for Information Technology, Symbiosis International University, Pune, India & School of Cybersecurity, Korea University, Seoul, South Korea
6 Ronin Institute, USA & International Center for AI and Cyber Security Research and Innovations, Asia University, Taichung, Taiwan
7 Department of Engineering Science, National Cheng Kung University, Tainan, Taiwan
8 Hong Kong Metropolitan University, Hong Kong & UCRD, Chandigarh University, Chandigarh, India & Center for Interdisciplinary Research, University of Petroleum and Energy Studies (UPES), Dehradun, India
Journal of Database Management; Hershey
Volume
36
Issue
1
Pages
1-25
Number of pages
26
Publication year
2025
Publication date
2025
Place of publication
Hershey
Country of publication
United States
Publication subject
ISSN
10638016
e-ISSN
15338010
Source type
Scholarly Journal
Language of publication
English
Document type
Journal Article
Publication history
Milestone dates
2025-01-01 (pubdate)
ProQuest document ID
3255275703
Document URL
https://www.proquest.com/scholarly-journals/hybrid-deep-learning-framework-intrusion/docview/3255275703/se-2?accountid=208611
Copyright
© 2025. This work is published under https://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Last updated
2025-12-15
Database
ProQuest One Academic