Password Guessing Based on LSTM Recurrent Neural Networks

被引：6

作者：

Xu, Lingzhi ^{[1
]}

Ge, Can ^{[1
]}

Qiu, Weidong ^{[1
]}

Huang, Zheng ^{[1
]}

Guo, Jie ^{[1
]}

Lian, Huijuan ^{[1
]}

Gong, Zheng ^{[2
]}

机构：

[1] Shanghai Jiao Tong Univ, Sch Informat Secur Engn, Shanghai, Peoples R China

[2] South China Normal Univ, Sch Comp Sci, Guangzhou, Guangdong, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1 | 2017年

关键词：

password guessing; recurrent neural network; LSTM;

D O I：

10.1109/CSE-EUC.2017.155

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Passwords are frequently used in data encryption and user authentication. Since people incline to choose meaningful words or numbers as their passwords, lots of passwords are easy to guess. This paper introduces a password guessing method based on Long Short-Term Memory recurrent neural networks. After training our LSTM neural network with 30 million passwords from leaked Rockyou dataset, the generated 3.35 billion passwords could cover 81.52% of the remaining Rockyou dataset. Compared with PCFG and Markov methods, this method shows higher coverage rate.

引用

页码：785 / 788

页数：4

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