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WiFi Network Analysis using Wireshark and Python

Overview

This repository contains Python scripts for analyzing WiFi network data captured using Wireshark. The analysis covers various aspects such as frames, RTS/CTS frames, data rates, SNR, LAN utilization, throughput, congestion effects, client performance based on RTS/CTS, and other observations.

File Structure

  • question_a.py: Analysis of frames (control and data) seen on the network.
  • question_b.py: Analysis of RTS/CTS frames seen on the network.
  • question_c.py: Analysis of data rates used by clients on the network and changes over time.
  • question_d.py: Analysis of SNR on client links and the distribution of measured SNRs.
  • question_e.py: Computation of LAN utilization and throughput using the methodology from [Jardosh+05], with observations on network congestion effects.
  • question_f.py: Analysis of client performance discrepancies based on RTS/CTS usage.

Usage

  1. Clone the repository to your local machine.
    git clone https://github.com/your-username/WiFi-Congestiion.git
    cd WiFi-Congestiion
  2. Install the required dependencies, including Python and necessary libraries (e.g., pandas, matplotlib).
     pip install pandas matplotlib
  3. Run each Python script (question_a.py to question_g.py) to perform the respective analysis and generate plots.
    python question_a.py
    python question_b.py
    # Repeat for other scripts
    

Notes

  • Each Python script contains the relevant tshark command used to generate the CSV file for analysis in the following format.
    !tshark -r your_capture_file.pcap -Y "your_filter_expression" -T fields -e your_fields -E separator=, > output_file.csv
    
  • Plots generated by the scripts visualize trends and provide insights into network performance and behavior.
  • Feel free to explore and modify the scripts to suit your analysis requirements or add new functionalities.

Authors

This application was created by Evangeli Silva esilva2@albany.edu, Abid Khawaja akhawaja@albany.edu and Isaac Menis imenis@albany.edu as part of the ICSI 525 course at the University at Albany SUNY.

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