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PREDICTING PASSWORD STRENGTH BASED ON NATURAL LANGUAGE PROCESSING TECHNIQUE
ABSTRACT
The first line of security against unwanted access to your computer and personal information is provided by passwords. Although there are many alternatives to passwords for access control, in many situations a password is a more convincing way to authenticate the identity. They reflect an individual’s identity for a system and offer a straightforward, direct method of system protection. As a result, passwords are now used for numerous functions in daily life. accessing programs, databases, networks, and websites, and even reading the morning newspaper online. getting an email from the server. transferring money. buying online. Every day, the need of choosing and utilizing strong passwords increases. In this research, the prediction of password strength is treated as a classification problem and Techniques for supervised machine learning were used. The model was developed using several well-known supervised machine learning methods, including Logistic Regression, Naive Bayes Classifier, Support Vector Machine, and Gradient Boosting Algorithms. The suggested model was tested against two separate datasets, and it was shown to be accurate and stable. The models’ outcomes were contrasted with those of the tools already in use for evaluating password strength. The results demonstrate that the machine learning method is significantly capable of classifying the extreme cases—Strong, Medium, and Weak passwords.
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