Protecting passwords from breaches

The Data

In our efforts to enhance online security, we have developed a password breach detection model designed to evaluate the strength and reliability of passwords. Our model was trained on a diverse dataset of over 100 million passwords. This dataset included a wide range of password types, from highly secure, complex passwords to common, easily guessable dictionary words and previously breached passwords. It included over 50 million passwords that have been previously breached, thus providing us with critical insights into patterns used by threat actors to exploit password weaknesses.

The Process

Before diving into the creation and training of our model, it was first necessary to preprocess and combine all of our extensive data. This involved several crucial steps to ensure the quality and relevance of the dataset:

1. Data Cleaning:

We carefully cleaned the dataset to remove any duplicates and irrelevant entries. This step is to prevent skewed training results and ensure the model learned from unique password instances.
2. Categorizing Passwords:

The dataset was then categorized into three main groups:

1. Strong Passwords: Complex passwords featuring a mix of uppercase and lowercase letters, numbers, and special characters. These passwords did not include dictionary words, or guessable passwords.

2. Vulnerable Passwords: These were passwords that were less likely to be guessed or breached, but were still vulnerable to future breaches.

3. Extremely Vulnerable Passwords: Passwords that have been compromised in data breaches, and passwords that can be easily deciphered using dictionary attacks.

Training

With our dataset preprocessed and labeled, we started to train our model. Our training process involved several key steps to ensure the great performance of our model:

1. Defining the Labels:

Each password was assigned a strength value ranging from 0 to 2, to help the model learn patterns related to the strength of any given password:

0: Weak and easily breached passwords, typically common dictionary words or previously breached passwords.

1: Moderately strong passwords that might have some complexity but still possess weaknesses.

2: Strong passwords with high complexity and a low likelihood of being breached.
2. Splitting the Data:

We divided the dataset into training, validation, and test sets. The training set was used to train the model, the validation set to fine-tune hyperparameters, and the test set to evaluate the final performance of the model, given a password from the test set.
3. Training the Model:

We employed a supervised learning algorithm, allowing the model to learn from the labeled dataset. The model was then trained over multiple epochs, adjusting its parameters to minimize the error in predicting password strength and breach likelihood.
4. Evaluating the Model:

After training, we evaluated the model on the test set to ensure its ability to generalize to new, unseen passwords. We used metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance.

The Impact

Our password breach detection model can now detect the strength of any given password, based on the likelihood of a breach, the complexity of the password, or if its strength is compromised by its use of dictionary words. This capability offers several significant advantages for enhancing online security:

1. Enhanced Security Assessments:

The model provides a quick and reliable assessment of password strength, helping users and administrators identify and improve weak passwords, automating security checks.
2. Proactive Breach Prevention:

By identifying passwords that are likely to be breached, the model enables proactive measures to prevent potential security incidents.
3. User Education:

The model can serve as an educational tool, guiding users towards creating stronger, more secure passwords by highlighting the characteristics of strong passwords, that are less likely to be breached.
4. Scalable Security Solutions::

Given its ability to analyze millions of passwords efficiently, the model is suitable for integration into large-scale applications and services, providing widespread security benefits.

Conclusion