Increase Security by Analyzing Password Strength using Machine Learning

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Authors
Asaduzzaman, Abu
D'Souza, Declan
Uddin, Md Raihan
Woldeyes, Yoel
Advisors
Issue Date
2024
Type
Conference Paper
Keywords
Computer security , Machine learning , Multiclass classification , Password , TF-IDF scores
Research Projects
Organizational Units
Journal Issue
Citation
Asaduzzaman, A., D'Souza, D., Uddin, M.R., Woldeyes, Y. Increase Security by Analyzing Password Strength using Machine Learning. (2024). Proceedings - 2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024, pp. 32-37. DOI: 10.1109/ECTIDAMTNCON60518.2024.10479995
Abstract

Passwords (and login credentials) are pivotal in our daily online activities, securing 'things' including emails and e-wallets. Yet, users tend to recycle similar passwords across multiple services, posing a significant security threat. The prevalence of easily guessed passwords exacerbates the security issues. This study proposes a machine learning model leveraging Term Frequency - Inverse Document Frequency (TF-IDF) scores that helps develop stronger passwords by analyzing how frequently specific characters appear in passwords. The proposed methodology involves utilizing logistic regression and the 000WebHost leaked password wordlist for training and validation. The outcomes of the machine learning model accurately assess users' password strength, potentially bolstering the security of their diverse online accounts. © 2024 IEEE.

Table of Contents
Description
Publisher
Institute of Electrical and Electronics Engineers Inc.
Journal
Proceedings - 2024 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024
Book Title
Series
Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024
31 January 2024 through 3 February 2024
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ISSN
EISSN