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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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Details
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