OMECNN: a password-generation model based on ordered markov enumerator and critic neural network

Abstract:

Password identification is one of the most popular way of identification. Generating a large-scale password set based on password-generation techniques is a principal method to research password security, which can be applied to evaluate the efficiency of password-generation algorithm and detect the defects of existing user-password protective mechanisms. In this paper, we propose a password-generation model based on an ordered Markov enumerator and critical neural network (OMECNN). The OMECNN model combines both Markov chain and neural network techniques. OMECNN utilizes the ordered Markov passwords enumerator to generate the passwords according to the probability of combinations, and then uses the critic neural network to score those passwords, and selects the passwords whose score is higher than the threshold to form the final password set. The generated password set has the characteristics of sorting according to the combination probability of passwords and the distribution of passwords in accordance with the real training password set. The experimental results show that when 1e7 passwords are generated, the hits of OMECNN model on Rockyou test set is 16.60% higher than that of OMEN model and 220.02% higher than that of Pass GAN model.