Intrusion Detection System (IDS)

This project implements an Intrusion Detection System using machine learning techniques, specifically an LSTM Autoencoder for anomaly detection and a classifier for attack type identification.

Features

• Real-time network traffic analysis
• Anomaly detection using LSTM Autoencoder
• Attack classification using trained models
• Web-based interface built with Streamlit

Installation

1. Clone the repository:

   git clone https://github.com/Karan27q/ids.git
   cd ids
   

2. Install dependencies:

   pip install -r requirements.txt
   

Usage

Running the Web App

To start the Streamlit application:

streamlit run app.py

This will launch the web interface where you can upload network traffic data for analysis.

Training Models

The app.ipynb notebook contains the code for training the models. Run the cells in order to:

• Load and preprocess data
• Train the LSTM Autoencoder
• Train the attack classifier
• Evaluate and save models

Files

• app.py: Streamlit web application
• app.ipynb: Jupyter notebook for model training
• attack_classifier.keras: Trained attack classification model
• lstm_autoencoder.keras: Trained LSTM autoencoder model
• scaler.save: Saved data scaler
• requirements.txt: Python dependencies
• test.py: Test script

Dependencies

• streamlit
• tensorflow
• scikit-learn
• joblib
• numpy
• pandas
• matplotlib

License

[Add license information here]