GENPass: A Multi-Source Deep Learning Model For Password Guessing

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dc.contributor.authorXia, Zhiyang
dc.contributor.authorYi, Ping
dc.contributor.authorLiu, Yunyu
dc.contributor.authorJiang, Bo
dc.contributor.authorWang, Wei
dc.contributor.authorZhu, Ting
dc.date.accessioned2019-10-04T14:33:34Z
dc.date.available2019-10-04T14:33:34Z
dc.date.issued2019-09-11
dc.description.abstractThe password has become today’s dominant method of authentication. While brute-force attack methods such as HashCat and John the Ripper have proven unpractical, the research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic contextfree grammar (PCFG) are all based on statistical probability. These approaches require a large amount of calculation, which is time-consuming. Neural networks have proven more accurate and practical in password guessing than traditional methods. However, a raw neural network model is not qualified for crosssite attacks because each dataset has its own features. Our work aims to generalize those leaked passwords and improves the performance in cross-site attacks. In this paper, we propose GENPass, a multi-source deep learning model for generating “general” password. GENPass learns from several datasets and ensures the output wordlist can maintain high accuracy for different datasets using adversarial generation. The password generator of GENPass is PCFG+LSTM (PL). We are the first to combine a neural network with PCFG. Compared with Long short-term memory (LSTM), PL increases the matching rate by 16%-30% in cross-site tests when learning from a single dataset. GENPass uses several PL models to learn datasets and generate passwords. The results demonstrate that the matching rate of GENPass is 20% higher than by simply mixing datasets in the cross-site test. Furthermore, we propose GENPass with probability (GENPass-pro), the updated version of GENPass, which can further increase the matching rate of GENPass.en_US
dc.description.sponsorshipThis work is supported by the National Natural Science Foundation of China(61571290, 61831007, 61431008), National Key Research and Development Program of China (2017YFB0802900, 2017YFB0802300, 2018YFB0803503), Shanghai Municipal Science and Technology Project under grant (16511102605, 16DZ1200702), NSF grants 1652669 and 1539047.en_US
dc.format.extent10 pagesen_US
dc.genreJournal Articlesen_US
dc.identifierdoi:10.13016/m2nw2y-hqaw
dc.identifier.citationZ. Xia, P. Yi, Y. Liu, B. Jiang, W. Wang and T. Zhu, "GENPass: A Multi-Source Deep Learning Model for Password Guessing," in IEEE Transactions on Multimedia. doi: 10.1109/TMM.2019.2940877 keywords: {Password;Neural networks;Deep learning;Gallium nitride;Training;Computational modeling;Markov processes}, URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8832180&isnumber=4456689en_US
dc.identifier.urihttps://doi.org/10.1109/TMM.2019.2940877
dc.identifier.urihttp://hdl.handle.net/11603/14976
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.subjectPassworden_US
dc.subjectNeural networksen_US
dc.subjectDeep learningen_US
dc.subjectGallium nitrideen_US
dc.subjectTrainingen_US
dc.subjectComputational modelingen_US
dc.subjectMarkov processesen_US
dc.titleGENPass: A Multi-Source Deep Learning Model For Password Guessingen_US
dc.typeTexten_US

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