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Related Concept Videos

Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Related Experiment Video

Updated: May 15, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Novel approach of encrypted network traffic classification using deep convolutional neural network with Artificial

Sujan Kumar Mohanty1,2, Satyajit Rath1,2, Satya Ranjan Sahu1,2

  • 1Academy of Scientific and Innovative Research, Ghaziabad, India.

Frontiers in Big Data
|May 14, 2026
PubMed
Summary

This study introduces a hybrid Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) model for classifying VPN and non-VPN encrypted network traffic. The model achieves high accuracy, outperforming traditional methods for real-time network security monitoring.

Keywords:
Artificial Bee ColonyDCNNGenetic AlgorithmVPN detectionencrypted trafficfeature selectionnetwork traffic classificationoptimization

Related Experiment Videos

Last Updated: May 15, 2026

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

  • Computer Science
  • Network Security
  • Artificial Intelligence

Background:

  • Encrypted network traffic poses challenges for dynamic classification and security monitoring.
  • Existing methods struggle with the complexity and volume of modern network data.

Purpose of the Study:

  • To develop and evaluate a hybrid Deep Convolutional Neural Network (DCNN) and Long Short-Term Memory (LSTM) model for classifying VPN and non-VPN encrypted traffic.
  • To optimize the model using Artificial Bee Colony (ABC) and Genetic Algorithm (GA) for enhanced performance.

Main Methods:

  • Utilized the QUIC dataset for training and validation.
  • Implemented a hybrid DCNN+LSTM architecture with spatio-temporal feature extraction.
  • Applied dual metaheuristic optimization (ABC and GA) for global hyperparameter and structural optimization.
  • Employed feature selection techniques (correlation analysis, Fisher Score, mutual information) to identify key features (Size, Batch Cache, Delta Previous Packet).
  • Addressed class imbalance using weighted loss functions and stratified data division.

Main Results:

  • Achieved a classification accuracy of 99.66%, with ROC-AUC of 0.994 and PR-AUC of 0.987.
  • Demonstrated superior performance compared to traditional classifiers (Decision Tree, Random Forest, SVM, KNN), individual deep-learning models (CNN, LSTM), and the FlowPic method.
  • Confirmed consistent generalization with a mean accuracy of 99.53% ± 0.09% via stratified cross-validation.
  • An ablation study validated the contribution of each model component.

Conclusions:

  • The proposed hybrid DCNN+LSTM framework effectively classifies encrypted network traffic.
  • The dual metaheuristic optimization strategy enhances model robustness and accuracy.
  • This framework is suitable for real-time monitoring of security-sensitive encrypted networks.