Optimized hybrid CNN-LSTM-GRU model for cyber attack detection using Adaptive Equilibrium Optimization
Year: 2025
Type: article
Abstract: Modern network security has a great difficulty in cyber attack detection. In this context, this work presents an CNN-LSTM-GRU Model for cyber attack detection. AEO was used to maximize feature selection for the model, therefore lowering unnecessary data while maintaining important attack patterns. With 98% accuracy, the suggested model beats conventional GRU, LSTM, and RNN architectures according to experimental data. Computationally efficient for the proposed model achieves equivalent accuracy to the Transformer model with less number of FLOPs and parameters.
Source: ICT Express
Institutions: Asia University, China Medical University, Korea University, University of Johannesburg, Symbiosis International University, China Medical University Hospital, Chaoyang University of Technology, Ronin Institute, Chandigarh University, University of Petroleum and Energy Studies, Hong Kong Metropolitan University, King Abdulaziz University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
Award: G1709758191
OpenAlex ID: W4415215985