Revisiting Monte Carlo Strength Evaluation

TL;DR

This paper enhances the Monte Carlo method for password strength estimation by improving sampling precision and introducing precomputation techniques to increase speed with minimal memory overhead.

Contribution

It proposes novel improvements to the Monte Carlo approach, focusing on better sampling and precomputation for more efficient password strength evaluation.

Findings

Improved sampling yields slightly better precision.

Precomputation accelerates estimations with modest memory increase.

Experimental results confirm the effectiveness of the proposed methods.

Abstract

The Monte Carlo method, proposed by Dell'Amico and Filippone, estimates a password's rank within a probabilistic model for password generation, i.e., it determines the password's strength according to this model. We propose several ideas to improve the precision or speed of the estimation. Through experimental tests, we demonstrate that improved sampling can yield slightly better precision. Moreover, additional precomputation results in faster estimations with a modest increase in memory usage.