To classify IoT and non-IoT Devices by characterizing and profiling network traffic traces.
Contents of the folder:
1.Source Code - Execute the .ipynb file in Jupyter Notebook - Please execute all cells in sequence - The code is commented for understanding
2.IoT_pcap folder - Input folder - keep all the input pcap files in this folder
3.Database - Group24_NS_Project.db - vew in DB Browser tool
4.List_Of_Devices.txt - text file containing all devices and their respective MAC addresses
5.Presentation PPT
Dependencies: Jupyter Notebook IDE - To execute code
Scapy - To parse the .pcap files and extract the necessary data/features into the SQLite tables.
SQLite - To store data in sqlite database.
Pandas - To clean and preprocess data.
Numpy - To work with arrays.
Matplotlib - To plot graphs, histograms to represent the results visually.
Scource Code Files:
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PcapToDB.ipynb - Run this first on pcap files to save data into database
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ExtractFeatures.ipynb - Run this file after PcapToDB.ipynb. Extract the features of the dataset and compare the graphs of IoT(Light Bulb) and non-IoT device(Macbook - other devices were not active on that day) As mentioned in paper, the data of Light bulb of 28th September 2016 was used.
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Graph.ipynb - Plot graphs of ports and frequencies
Download pcap data from opensource - https://iotanalytics.unsw.edu.au/iottraces