Password Strength Prediction using Regression-Based Machine Learning Models with Entropy and Caesar Cipher–Driven Synthetic Dataset

Abstract: In today’s digital era, weak and predictable passwords remain a major cause of cybersecurity breaches. This paper presents a novel machine learning–based password strength prediction model using regression techniques on a synthetically generated dataset enhanced with Caesar cipher transformations, incorporating features such as length, character composition, whitespace inclusion, and entropy. Linear, Random Forest, and Gaussian Process Regression models are compared, with Gaussian Regression achieving the highest accuracy (R² = 0.9998), providing a scalable and interpretable framework for real-time password strength evaluation.

Keywords: Password Strength Evaluation, Machine Learning, Regression Analysis, Shannon Entropy, Password Security, Caesar Cipher, Random Forest Regression, Gaussian Process Regression, Synthetic Dataset, Cybersecurity, Feature Engineering.