IoT Identification

Table of Contents

1. Project Overview
2. Installation
3. Limitations and Further Research

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Project Overview

The aim of this project is to develop a machine learning model to identify an IoT device based on DNS logs from a Wi-Fi access point.

The repository proposes 2 mathematically equivalent Random Forest classifiers, achieving an accuracy of 97%. The first proposal is multi class random forest classifier, whereas the second implementation is an array of binary random forest classifiers. The purpose of the second model is to simplify adding classes to the model without retrainining the entire model.

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Installation

1. Create a virtual environment

MacOS / Linux:

python -m venv venv
source venv/bin/activate

Windows:

python -m venv venv
venv\Scripts\activate

2. Install the requirements

pip install -r requirements.txt

3. Install the project in editable mode

pip install -e .

4. Run the model with the default features

python -m src.identification.feature_extraction.py
python -m src.identification.binary_model.py

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Limitations and Further Research

• Potential overfitting in certain cases.
• Data drift
• Model degredation with new classes (binary model)