ADAPTIVE NETWORK TRAFFIC GENERATOR (ATG)

A network traffic generation and validation platform designed to simulate real-world traffic, perform packet capture, validate delivery, and analyze network performance through an integrated pipeline.

This system was developed as part of a cybersecurity research and engineering initiative focused on traffic validation, execution orchestration, and end-to-end network behavior analysis.

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Table of Contents

• Overview
• System Architecture
• Execution Workflow
• Project Structure
• Features
• Execution Pipeline
• Technologies Used
• Hardware Requirements
• Installation Instructions
• Running the Application
• API Reference
• Validation & Analysis Pipeline
• Contributing
• Code of Conduct
• Security Policy
• Community & Support

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Overview

The Adaptive Network Traffic Generator (ATG) enables developers, researchers, and network engineers to simulate controlled network traffic and validate delivery using both capture-based and receiver-based mechanisms.

The system integrates traffic generation, packet capture, receiver-side validation, and analysis into a unified workflow.

The platform provides the following capabilities:

• Protocol-based traffic generation (ICMP, TCP, HTTP, HTTPS, SSH)
• Execution lifecycle-controlled packet capture
• PCAP-based traffic analysis
• Receiver-side validation for accurate delivery verification
• Duplicate, sequence, and latency tracking
• Execution control and scheduling
• Modular architecture across multiple levels (Level-0, Level-1, Level-2)
• API-driven execution and monitoring
• Persistent storage of execution results and metrics

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System Architecture

Level-1 (Sender + Capture Model)

flowchart LR
    Sender --> Network --> CaptureManager --> Analyzer --> Metrics

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Level-2 (Receiver Validation Model – Implemented)

flowchart LR
    Sender --> Network --> ReceiverAgent --> MetricsEngine --> Database

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Backend Architecture

flowchart LR
    UI[Frontend] --> API[FastAPI]

    API --> ExecutionManager
    API --> Scheduler

    ExecutionManager --> ExecutionEngine

    ExecutionEngine --> Sender
    ExecutionEngine --> CaptureManager
    ExecutionEngine --> Analyzer

    Sender --> Network
    Network --> ReceiverAgent

    CaptureManager --> PCAP
    Analyzer --> Metrics

    ReceiverAgent --> MetricsEngine
    MetricsEngine --> Database

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Execution Workflow

sequenceDiagram
    participant UI
    participant API
    participant EM
    participant EE
    participant CAP
    participant SND
    participant ANA
    participant RCV
    participant DB

    UI->>API: Start Execution
    API->>EM: start_job()

    EM->>EE: start()
    EE->>CAP: start_capture()
    EE->>SND: generate traffic

    SND-->>Network: send packets
    Network-->>RCV: receive packets

    EE->>CAP: stop_capture()
    EE->>ANA: analyze(pcap)

    RCV->>DB: validation metrics
    EE->>DB: execution results

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Project Structure

backend/

├── level0/
├── level1_backend/
├── level2/

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Features

Traffic Generation

• Supports ICMP, HTTP, HTTPS, SSH
• Configurable traffic parameters

Packet Capture

• Real-time capture using Scapy
• Execution-synchronized capture lifecycle

Receiver Validation

• Latency tracking
• Duplicate detection
• Sequence validation
• Packet integrity verification

Packet Analysis

• Protocol distribution
• Delivery percentage
• PCAP-based inspection

Execution Control

• Start / Pause / Resume / Stop

Scheduling

• One-time and interval execution

Storage

• MongoDB-based persistence

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Execution Pipeline

1. Execution request initiated via API
2. ExecutionManager schedules job
3. ExecutionEngine starts traffic generation
4. CaptureManager starts packet capture
5. Traffic flows through network
6. ReceiverAgent validates packets
7. Capture stops and PCAP is analyzed
8. Metrics stored in database

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Technologies Used

Backend

• Python
• FastAPI
• Scapy

Frontend

• React

Database

• MongoDB

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Hardware Requirements

Minimum

• Dual Core CPU
• 4 GB RAM

Recommended

• Quad Core CPU
• 8–16 GB RAM

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Installation Instructions

git clone https://github.com/ANair97/Adaptive-Network-Traffic-Generator.git
cd Adaptive-Network-Traffic-Generator

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Backend

cd backend
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt

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Frontend

cd frontend
npm install
npm run dev

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Running the Application

Backend

http://localhost:8000/docs

Frontend

http://localhost:3000

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API Reference

Execution

• POST /execute
• GET /executions
• GET /executions/{job_id}

Control

• POST /pause/{job_id}
• POST /resume/{job_id}
• POST /stop/{job_id}

Analysis

• GET /executions/{job_id}/pcap
• GET /executions/{job_id}/headers

Scheduling

• POST /schedule/once
• POST /schedule/interval

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Validation & Analysis Pipeline

1. Traffic generation
2. Packet capture
3. Network transmission
4. Receiver validation
5. PCAP analysis
6. Metrics computation
7. Data storage

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Contributing

Please refer to CONTRIBUTING.md before submitting changes.

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Code of Conduct

All contributors are expected to maintain a respectful and professional environment.

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Security Policy

Report vulnerabilities privately. Do not open public issues for security concerns.

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Community & Support

Channel	Purpose
GitHub Issues	Bug reports & feature requests
GitHub Discussions	General questions, ideas, and community chat
Email — office.isfcr@pes.edu	Academic & research collaboration inquiries
Email — anikaitm752@gmail.com	Direct maintainer contact

Note: This project is developed and maintained as part of cybersecurity research at PES University (ISFCR). Response times may vary during academic periods.

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© Copyright 2026 PES University.

Authors:

Anikait Nair - anikaitm752@gmail.com

Dr. Swetha P - swethap@pes.edu

Dr. Prasad B Honnahalli - prasadbh@pes.edu

Contributors:

PurpleSynapz - info@purplesynapz.com

Licensed under the Apache License, Version 2.0 (the "License"); You may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

SPDX-License-Identifier: Apache-2.0

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For further queries related to the project/application, reach out to ISFCR, PES University - office.isfcr@pes.edu