Password Keystroke Authenticator

Inspiration

What it does# Keystroke Authentication System

This project implements a behavioral biometric system using keystroke dynamics. It analyzes typing patterns (specifically press/release timing) to distinguish between real users and imposters using a machine learning model.

📚 Overview

The system collects how users type a password and trains a classifier to verify identity based on typing rhythm. It uses features like dwell time (how long a key is held) and timing between key presses.

🧠 How It Works

1. Keystroke Data Collection

   • User types a password.
   • Press and release timestamps are recorded.
   • Timing features are extracted (e.g., dwell time).

2. Data Labeling

   • True user samples are labeled 1.
   • Imposter (or variation) samples are labeled 0.

3. Data Production

   • Synthetic data is produced using the user's inputs
   • The data is given a range of random values based on the keystroke timings

4. Model Training

   • A Random Forest Classifier is trained using the labeled data.

5. Prediction

   • New keystroke samples are processed and passed to the trained model for prediction.

❌ Current Limitations

• Synthetic Data: The current implementation uses synthetic data for training and testing. This means the model is not yet based on actual user input or real-world data. The synthetic data is generated with randomized values, which may not fully represent the performance of the model when working with real user inputs.

• Limited User Sampling: The system has not yet been fully configured to handle extensive or diverse user sampling. While it supports basic functionality for recording keystrokes, it is not optimized for large-scale user input collection. More work is needed to make it adaptable to varied user behavior, such as handling multiple samples, different typing speeds, or irregular patterns.

🐍 Dependencies

• Python 3.10+
• pandas
• numpy
• scikit-learn
• pynput

Install them with:

pip install pandas numpy scikit-learn pynput

How to run:

python main.py

Created by Brendan Moore and Aidan Sundt for Loyola University Maryland Hackathon 2025

Built With

• numpy
• pandas
• pycharm
• python
• scikit-learn