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Related Experiment Videos

Enhancing encrypted HTTPS traffic classification based on stacked deep ensembles models.

Ahmed M Elshewey1, Ahmed M Osman2

  • 1Department of Computer Science, Faculty of Computers and Information, Suez University, P.O.Box:43221, Suez, Egypt.

Scientific Reports
|October 9, 2025
PubMed
Summary
This summary is machine-generated.

Classifying encrypted HTTPS traffic is essential for network security. Ensemble learning, combining deep learning models like CNN, achieved state-of-the-art accuracy for encrypted traffic analytics.

Keywords:
CNNCyber securityDNNDeep learningEncrypted trafficEnsemble learningHTTPS traffic classificationNetwork securityNetwork traffic classification

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Area of Science:

  • Network Security
  • Machine Learning
  • Data Science

Background:

  • Traditional methods for classifying network traffic are ineffective against encrypted HTTPS.
  • Evolving traffic patterns further complicate network management and security analysis.

Purpose of the Study:

  • To develop and evaluate a robust framework for classifying encrypted HTTPS traffic.
  • To benchmark deep learning models and explore ensemble methods for improved accuracy and reliability.

Main Methods:

  • Utilized a public Kaggle dataset with 145,671 flows and 88 features across six traffic categories.
  • Developed an automated preprocessing pipeline including data normalization, stratified splitting, and imbalance-aware weighting.
  • Benchmarked deep learning architectures (DNN, CNN, RNN, LSTM, GRU) and implemented a stacked ensemble meta-learner.

Main Results:

  • Convolutional Neural Network (CNN) demonstrated strong single-model performance (Accuracy 0.9934).
  • A stacked ensemble meta-learner achieved state-of-the-art results (Accuracy 0.9949, F1_macro 0.9932).
  • The framework provides interpretable outputs like confusion matrices and ROC curves.

Conclusions:

  • Ensemble learning significantly enhances the performance of encrypted traffic classification compared to individual models.
  • The publicly available codebase ensures reproducibility and facilitates practical deployment of the traffic analytics pipeline.
  • The study offers a deployment-ready solution for advanced encrypted traffic analysis in network management and security.