Abstract: Password is one of the most important methods of user authentication.Using effective password guessing methods to improve the hit rate of password attacks is the main approach to study password security.In recent years, researchers have proposed to use long short-term memory (LSTM) neural network to guess password and have demonstrated it is superior to traditional password guessing models, such as Markov model and PCFG(probabilistic context free text) model.However, the traditional LSTM model has the problem that it is hard to select the length of the sequence and cannot learn the relationship between different length sequences.This paper collects large-scale password sets and analyzes the user's password construction behaviors and the preference for passwords setting, and finds that the user's personal information has important influences on the password settings.Then a multiple sequence lengths of LSTM password guessing model MLSTM(Multi-LSTM) is proposed and the personal information is applied to trawling guessing.Experimental results demonstrate that compared with PCFG, the cracking rate is increased by 68.2% at most.While compared with traditional LSTM and 3th-order Markov, the hit rates are increased by 7.6%~42.1% and 23.6%~65.2% respectively.

Key words: Neural network, Password analysis, Password guessing, Password security, User information