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Passwords are the most widely used authentication method and play an important role in users’ digital lives. Password guessing models are generally used to understand password security, yet statistic-based password models (like the Markov model and probabilistic context-free grammars (PCFG)) are subject to the inherent limitations of overfitting and sparsity. With the improvement of computing power, deep-learning based models with higher crack rates are emerging. Since neural networks are generally used as black boxes for learning password features, a key challenge for deep-learning based password guessing models is to choose the appropriate preprocessing methods to learn more effective features.

To fill the gap, this paper explores three new preprocessing methods and makes an attempt to apply them to two promising deep-learning networks, i.e., Long Short-Term Memory (LSTM) neural networks …