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

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 <1.5% false positives. The system’s adaptive countermeasures, including honeypot redirection, traffic throttling, and quarantine protocols, effectively neutralized threats while maintaining operational continuity. Notably, the biologically-inspired trophallaxis mechanism reduced overfitting by 12% compared to traditional dropout methods by optimizing neuron-level learning dynamics. These results demonstrate the system’s effectiveness in combating sophisticated deception-based attacks while maintaining practical deploy ability in real-world cybersecurity operations.