A Hybrid Deep Learning and Genetic Algorithm Model for Explainable Network Traffic Classification
Abstract
This thesis presents an explainable deep learning model for network traffic classification using a genetic algorithm. A ResNet-based classifier is combined with a GA-driven dominant feature selection method to enhance interpretability and optimize accuracy. Experiments on real-world encrypted traffic datasets achieved 97.24% accuracy while identifying critical statistical features. The model effectively balances accuracy, simplicity, and transparency, contributing to the advancement of explainable artificial intelligence in network security analysis.
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References
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