How can you implement keystroke dynamics in computer engineering projects?

1 What is keystroke dynamics?

Keystroke dynamics measures the time intervals between each keystroke and the duration of each key press. These metrics can form a unique signature for each user, as different people have different typing styles and habits. Keystroke dynamics can be used to verify the identity of a user during login, or to monitor the user's activity throughout a session. Keystroke dynamics can also detect impostors, anomalies, and frauds by comparing the current typing pattern with the expected one.

3 How to collect keystroke data?

To implement keystroke dynamics, you need to collect keystroke data from the users. You can use various tools and methods to capture the keystroke events, such as keyboard hooks, event listeners, or web APIs. For example, in Python, you can use the PyKeystroke library to record the keystroke data from any keyboard input. PyKeystroke provides a simple and flexible interface to access the raw keystroke data, such as the key name, the key code, the event type, and the event timestamp. You can install PyKeystroke with pip: pip install pykeystroke

5 How to analyze keystroke data?

After you have processed the keystroke data, you need to analyze it to perform authentication tasks, such as enrollment, verification, or identification. You can use various techniques and algorithms to compare and classify the keystroke features, such as statistical methods, machine learning models, or neural networks. For example, to perform verification using a simple threshold-based method, you can use the following code:

from pykeystroke import KeystrokeData
data = KeystrokeData()
data.record_word("hello")
reference = data.get_feature_vector()
data.record_word("helo")
query = data.get_feature_vector()
threshold = 0.1
score = data.compare_vectors(reference, query)
if score < threshold:
  print("Authenticated")
else:
  print("Rejected")        

6 How to improve keystroke dynamics?

Keystroke dynamics is not a perfect authentication method, as it can be affected by various factors like typing speed, mood, fatigue, or environment. To improve performance and accuracy, you should apply some best practices and enhancements. For example, use a large and diverse dataset to train and test your keystroke dynamics model. Additionally, multiple features and metrics should be used to capture the keystroke dynamics such as n-grams, pressure, or angle. Advanced and adaptive models can also be used to learn and update the keystroke dynamics like deep learning, fuzzy logic, or reinforcement learning. Finally, multimodal and hybrid authentication methods can be employed to combine keystroke dynamics with other biometrics like face, voice, or fingerprint.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?