Machine Learning Based Password Strength Analysis
Sony Kuriakose1, G Krishna Teja2, Sravan Duggi3, A Harshel Srivatsava4, Venkat Jonnalagadda5

1Mrs. Sony Kuriakose, Assistant Professor, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
2G Krishna Teja*, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
3A Harshel Srivatsava, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
4Sravan Duggi, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India.
5Venkat Jonnalagadda, Department of Information Science and Engineering, New Horizon College of Engineering, Bangalore (Karnataka) India. 
Manuscript received on 03 June 2022. | Revised Manuscript received on 08 June 2022. | Manuscript published on 30 July 2022. | PP: 5-8 | Volume-11 Issue-8, July 2022. | Retrieval Number: 100.1/ijitee.H91190711822 | DOI: 10.35940/ijitee.H9119.0711822
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Abstract: Passwords, as the most used method of authentication because to its ease of implementation, allow attackers to get access to the accounts owned by others by means of cracking passwords. This is cause of the similar patterns that users use to create a password, like dictionary words, common phrases, person and location names, keyboard pattern, and so on. Multiple password cracking techniques had been introduced to predict the password offline or online, with the majority of records say the one with weak password or familiar password patterns being cracked. This suggested prototype implements numerous machine learning methods such as Decision Tree (DT), Nave Bayes (NB), Logistic Regression (LR), and Random Forest (RF) on a web application in real time to force users to choose a secure password. This results in the user’s account being logged into if particularly the password strength from more than half of the algorithms is strong. 
Keywords: Passwords, Password Strength, Password Analysis, Machine Learning.
Scope of the Article: Machine Learning