Intrusion Detection System

This project implements a simple Intrusion Detection System (IDS) using machine learning. It allows users to upload CSV files containing network traffic data and checks for potential attacks.

Setup

1. Clone this repository.
2. Install the required dependencies:

   pip install -r requirements.txt
   

3. Train the model (make sure you have the dataset):

   python model/train_model.py
   

4. Run the Streamlit app:

   streamlit run app.py
   

Usage

1. Open the Streamlit app in your web browser.
2. Upload a CSV file containing network traffic data.
3. Click the "Check for Attacks" button to analyze the data.
4. View the results, including the percentage of detected attacks and a sample of attack records.

File Structure

• app.py: Main Streamlit application
• preprocessing/preprocess.py: Data preprocessing functions
• utils/helpers.py: Utility functions
• model/train_model.py: Script for training the machine learning model
• model/model.joblib: Saved machine learning model (generated after training)
• requirements.txt: Project dependencies
• README.md: Project documentation

Note

Make sure to train the model with your specific dataset before using the application. Adjust the preprocessing steps and model parameters as needed for your use case.