Markov Model for Password Attack Prevention

Abstract

With the rapid increase in multi-user systems, the strength of passwords plays a crucial role in password authentication methods. Password strength meters help the users for the selection of secured passwords. But existing password strength meters are not enough to provide high level of security that makes the selection of strong password by users. Rule-based methods that measure the strength of passwords fall short in terms of accuracy and password frequencies differ among platforms. Use of Markov model-based strength meters improves the strength of password in more accurate way than the existing state-of-the-art methods. This paper describes how to proactively evaluate passwords with a strength meter by using Markov models. A mathematical proof of the prevention of guessable password attacks is presented. The proposed method improves the accuracy of current password protection methods significantly with a simpler, faster, and more secure implementation.