WordMarkov: A New Password Probability Model of Semantics

ABSTRACT To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. We also reveal some interesting password habits from the semantics on “long” passwords. Based on those findings, WordMarkov achieves 75.35%– 96.34% attack improvement