关键词: 明文口令, 口令生成, 马尔可夫模型, 概率上下文无关语法, 神经网络

Abstract: In recent years, broad and profound research has been launched on plain passwords cracking and recovery. In general, mainstream approaches have greatly improved the success rate of password cracking and recovery, but there is a lack of comparison and internal correlation between different methods. This paper focuses on the generation of plaintext cipher dictionaries, summarizing three main kinds of competitive models for the past decade：Markov model, Probability Context-Free Grammar（PCFG） and Neural Network（NN） model. In order to evaluate these three mainstream models effectively and objectively, the paper designs and conducts a series of performance verification experiments, explains their occasions, advantages, disadvantages and reasons from the principle level, proposes some ideas to optimize and improve. In the end, it thinks that Neural Network will become the most promising model in the future, and points out that further research and development in this field depend on the establishment of normative data set and the combination of various methods.

Key words: plain password, password generation, Markov model, Probability Context-Free Grammar（PCFG）, Neural Network（NN）