A HYBRID DEEP LEARNING AND GENETIC ALGORITHM MODEL FOR EXPLAINABLE NETWORK TRAFFIC CLASSIFICATION

Authors

  • Ibrohimov Azizbek Ravshonbek ugli Head of Department, State Enterprise "Cybersecurity Center" Author
  • Haydarov Elshod Dilshod o‘g‘li Head of the department, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi Author

Keywords:

Deep Learning; Genetic Algorithm; Explainable Artificial Intelligence; Network Traffic Classification; Feature Selection; Residual Network (ResNet); Encrypted Traffic; Model Interpretability

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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Published

2025-10-27

Issue

2025: ICDE -TURKEY -OCTOBER

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

A HYBRID DEEP LEARNING AND GENETIC ALGORITHM MODEL FOR EXPLAINABLE NETWORK TRAFFIC CLASSIFICATION. (2025). Innovate Conferences, 56-59. https://innovateconferences.org/index.php/ic/article/view/449