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  • Deep Learning
  • Cybersecurity

Federated Deep Learning-Based VPN Traffic Classification

A deep learning system that classifies encrypted network traffic as VPN or non-VPN from behavioral flow features.

Overview

Eighty statistical flow features from the ISCX dataset allow classification without any payload inspection. CNN, LSTM and NIN models are trained — the LSTM reaching 98.82% accuracy — and combined into an ensemble for robustness and confidence scoring, with a real-time dashboard supporting bulk CSV processing, anomaly detection and metric visualization.

Problem Statement

Encrypted traffic cannot be inspected by payload, so classifying it means either breaking encryption or giving up. Network operators still need to know what is traversing their links.

Proposed Solution

Classify from behavioural flow statistics — timing, size and direction patterns — which survive encryption, and ensemble multiple architectures so the decision carries a confidence score.

Key Features

  • Flow-Based Feature Analysis

    80 statistical features from the ISCX dataset, no payload inspection needed.

  • CNN, LSTM & NIN Models

    LSTM achieves 98.82% classification accuracy.

  • Ensemble Prediction

    Combines all three models for robustness and confidence scoring.

  • Real-Time Web Dashboard

    Bulk CSV processing, anomaly detection, and metric visualization.

Technologies Used

Deep Learning

  • CNN
  • LSTM
  • Network-in-Network
  • TensorFlow / Keras

Networking

  • ISCX VPN dataset
  • Flow statistics
  • Feature extraction

Application

  • Flask
  • Plotly
  • Bulk CSV processing

Project Workflow

  1. 1

    Flow extraction

    Statistical features are computed per traffic flow.

  2. 2

    Model training

    CNN, LSTM and NIN models are trained on the features.

  3. 3

    Ensemble prediction

    Outputs are combined with confidence scoring.

  4. 4

    Dashboard

    Bulk results and anomalies are visualised in real time.

What You'll Receive

  • Complete, runnable source code with folder structure
  • Project report and technical documentation
  • Ready-to-present PPT content
  • Setup and installation walkthrough
  • Line-by-line project explanation session
  • Viva question bank with answers
  • Bug fixing and troubleshooting help
  • Post-delivery support after submission

Frequently Asked Questions

How much does this project cost?

Every project is quoted individually, because the price depends on the modules you need, your technology stack, your college's format and your deadline. Send us the project name on WhatsApp and we'll share a quote the same day.

Can the project be customised to my college requirements?

Yes. Share your guide's requirements, preferred technology stack and abstract format, and we adapt the modules, dataset or UI accordingly.

Will I be able to explain this project during my viva?

That is the point of the explanation session. We walk you through the architecture, every module, the flow of data and the results, and hand over a viva question bank with answers.

What if the project does not run on my laptop?

We help you with setup end to end — dependencies, environment, database and configuration — over chat or a call until it runs on your machine.

Do I get support after submission?

Yes. Post-delivery support is included, so you can come back for fixes, doubts or demo help even after the project is delivered.

Want This Project?

Get complete source code, documentation, PPT, explanation, and support.

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