Password Strength Checker Using Machine Learning
S. Kayalvili, T Devadharshini, N Dharaneesh, P Dhanush · 2024
The security of sensitive data heavily depends on the strength of user passwords. Traditional password regulations often fail to provide strong security against modern threats due to easily guessed or reused passwords. This paper presents a novel machine learning-based method for assessing password strength using algorithms that analyze various factors such as length, complexity, entropy, and patterns. Our methodology was tested on a diverse dataset, demonstrating a $20 \%$ improvement in password strength estimation accuracy compared to traditional heuristic-based methods. The proposed system, implemented on a web application, utilizes machine learning techniques like Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Random Forest (RF) to categorize passwords into Weak, Medium, and Strong. This approach ensures real-time feedback, aiding users in creating secure passwords.