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Predicting Password Strength Based on Natural Language Processing Technique

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dc.contributor.author Kabir, Ahsan
dc.date.accessioned 2022-11-28T03:41:59Z
dc.date.available 2022-11-28T03:41:59Z
dc.date.issued 22-08-01
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/9071
dc.description.abstract Passwords provide the first line of defense against unauthorized access to your computer and personal information. Though there are many alternatives to passwords for access control, a password is the more compellingly authenticating the identity in many applications. They provide a simple, direct means of protecting a system and they represent the identity of an individual for a system. So, life these days have become largely dependent on password for many purposes. Logging in to computer accounts, retrieving email from the server, transferring funds, online shopping, accessing programs, databases, networks, websites, and even reading the morning newspaper online. The problem of selecting and using good passwords is becoming more important every day. In this work password strength prediction is modeled as a classification task and supervised machine learning techniques were employed. Widely used supervised machine learning techniques namely Logistic Regression, Naïve Bayes Classifier, Support Vector Machine, and Gradient Boosting Algorithms were used for learning the model. The proposed model was applied to two different datasets and states that this model is stable in terms of accuracy. The results of the models were also compared with the existing password strength checking tools. The findings show that the machine learning approach has substantial capability to classify the extreme cases – Strong, Medium, and Weak passwords. © en_US
dc.language.iso en_US en_US
dc.publisher Daffodil International University en_US
dc.subject Algorithms en_US
dc.subject Logistic en_US
dc.subject Learning en_US
dc.title Predicting Password Strength Based on Natural Language Processing Technique en_US
dc.type Other en_US

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