Implementation of an Adaptive Cyber Deception Attack Management Using Deep Learning Framework

Odo Francisca E. and Asogwa T.C.
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Odo Francisca E.: Computer Science Department, Enugu State University of Science and Technology
Asogwa T.C.: Computer Science Department, Enugu State University of Science and Technology

International Journal of Research and Innovation in Applied Science, 2025, vol. 10, issue 7, 08-16

Abstract: This study presents an adaptive threat detection system that leverages Wide Area Neural Networks (WANN) enhanced with a novel trophallaxis-based regularization approach, developed through a Design Thinking-Agile hybrid methodology. The proposed 4-layer WANN architecture, utilizing Rectified Linear Unit (ReLU) activation and trained with Stochastic Gradient Descent (SGD) momentum backpropagation and batch normalization, demonstrated optimal performance with 89% training accuracy and 59% validation accuracy. The performance of the model demonstrates the model’s effectively balancing capacity in complexity and generalizability. When validated against real-world datasets from Ethnos Cyber Limited and ACE-SPED, the integrated system achieved 97.8% attack detection accuracy with

Date: 2025
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