This work is on a Creative Commons Attribution 4.0 International (CC BY 4.0) license, https://creativecommons.org/licenses/by/4.0/. Access to this work was provided by the University of Maryland, Baltimore County (UMBC) ScholarWorks@UMBC digital repository on the Maryland Shared Open Access (MD-SOAR) platform. Please provide feedback Please support the ScholarWorks@UMBC repository by emailing
[email protected] and telling us what having access to this work means to you and why it’s important to you. Thank you. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TMM.2019.2940877, IEEE Transactions on Multimedia GENPass: A Multi-Source Deep Learning Model For Password Guessing Zhiyang Xia∗, Ping Yi∗, Yunyu Liu∗, Bo Jiang∗, Wei Wangy, Ting Zhuy ∗School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China yDepartment of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, MD, 21250, USA Abstract—The password has become today’s dominant method useful for users to crack most passwords. Markov models of authentication. While brute-force attack methods such as [10] and probabilistic context-free grammar [11] are widely HashCat and John the Ripper have proven unpractical, the used techniques for password guessing. Both are based on research then switches to password guessing. State-of-the-art approaches such as the Markov Model and probabilistic context- probability models. Therefore, they are often computationally free grammar (PCFG) are all based on statistical probability.