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PassGAN: A Deep Learning Approach for Password Guessing [article]

Briland Hitaj, Paolo Gasti, Giuseppe Ateniese, Fernando Perez-Cruz
<span title="2019-02-14">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address this issue, in this paper we introduce PassGAN, a novel approach that replaces human-generated password rules with theory-grounded machine learning algorithms.  ...  When we evaluated PassGAN on two large password datasets, we were able to surpass rule-based and state-of-the-art machine learning password guessing tools.  ...  with a novel approach based on deep learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.00440v3">arXiv:1709.00440v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ali2vwbxpne75nxbqdncct4k2u">fatcat:ali2vwbxpne75nxbqdncct4k2u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200823092209/https://arxiv.org/pdf/1709.00440v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/83/5783d847e1e085e5b135b58c5ecdb2bc7c64d514.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.00440v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Password Guessing Based on GAN with Gumbel-Softmax

Tao Zhou, Hao-Tian Wu, Hui Lu, Peiming Xu, Yiu-Ming Cheung, Thi-Thu-Huong Le
<span title="2022-04-27">2022</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sdme5pnua5auzcsjgqmqefb66m" style="color: black;">Security and Communication Networks</a> </i> &nbsp;
The second is Gumbel-Softmax with temperature control for training GAN on passwords. Experimental results show the proposed G-Pass outperforms PassGAN in password quality and cracking rate.  ...  In this paper, we propose a novel password guessing model named G-Pass, which consists of two main components.  ...  In general, both types of approaches are unpractical for password guessing. With the advances of deep learning, applying deep learning technology to password guessing has become a hot topic.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2022/5670629">doi:10.1155/2022/5670629</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ybrgaf7js5cehnrndx7gobaxc4">fatcat:ybrgaf7js5cehnrndx7gobaxc4</a> </span>
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Recurrent GANs Password Cracker For IoT Password Security Enhancement

Sungyup Nam, Seungho Jeon, Hongkyo Kim, Jongsub Moon
<span title="2020-05-31">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
We studied some methods of improving the performance of PassGAN, and developed two approaches for better password cracking: the first was changing the convolutional neural network (CNN)-based improved  ...  Text-based passwords are a fundamental and popular means of authentication.  ...  Acknowledgments: This research was supported by a Korea University Grant. Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s20113106">doi:10.3390/s20113106</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32486361">pmid:32486361</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7309056/">pmcid:PMC7309056</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/timaoqxkfzarfdk7svvdngl4uy">fatcat:timaoqxkfzarfdk7svvdngl4uy</a> </span>
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Generating Optimized Guessing Candidates toward Better Password Cracking from Multi-Dictionaries Using Relativistic GAN

Sungyup Nam, Seungho Jeon, Jongsub Moon
<span title="2020-10-19">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
For instance, state-of-the-art password guessing studies such as PassGAN and rPassGAN adopted a Generative Adversarial Network (GAN) and used it to generate high-quality password guesses without knowledge  ...  In this paper, we suggest a deep learning-based approach called REDPACK that addresses the weakness of the cutting-edge cracking tools based on GAN.  ...  Acknowledgments: This research was supported by a Korea University Grant. Conflicts of Interest: The authors declare no conflict of interest. Appl. Sci. 2020, 10, 7306  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app10207306">doi:10.3390/app10207306</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ym5vkef77ngijhkp4l2dtqxwy4">fatcat:ym5vkef77ngijhkp4l2dtqxwy4</a> </span>
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PassFlow: Guessing Passwords with Generative Flows [article]

Giulio Pagnotta, Dorjan Hitaj, Fabio De Gaspari, Luigi V. Mancini
<span title="2021-12-14">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Data-driven password guessing approaches based on GANs, language models and deep latent variable models have shown impressive generalization performance and offer compelling properties for the task of  ...  In this paper, we propose PassFlow, a flow-based generative model approach to password guessing.  ...  Deep Latent Variable Models for Password Guessing Another generative approach proposed by Pasquini et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2105.06165v2">arXiv:2105.06165v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4jrnc3bjzfhunkajafu7ecfxk4">fatcat:4jrnc3bjzfhunkajafu7ecfxk4</a> </span>
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Improving Password Guessing via Representation Learning [article]

Dario Pasquini, Ankit Gangwal, Giuseppe Ateniese, Massimo Bernaschi, Mauro Conti
<span title="2020-07-26">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we introduce a deep generative model representation learning approach for password guessing.  ...  Based on these properties, we introduce:(1) A general framework for conditional password guessing that can generate passwords with arbitrary biases; and (2) an Expectation Maximization-inspired framework  ...  In Section II-B2, we introduce a different and novel deep generative model in the password guessing domain. 1) Improved GAN model: The password guessing approach presented in PassGAN suffers from an inherent  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.04232v3">arXiv:1910.04232v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kjl4aagx7rgp5jv6ap3brwotyy">fatcat:kjl4aagx7rgp5jv6ap3brwotyy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200810000649/https://arxiv.org/pdf/1910.04232v3.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.04232v3" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

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

Zhiyang Xia, Ping Yi, Yunyu Liu, Bo Jiang, Wei Wang, Ting Zhu
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/sbzicoknnzc3tjljn7ifvwpooi" style="color: black;">IEEE transactions on multimedia</a> </i> &nbsp;
In this paper, we propose GENPass, a multisource deep learning model for generating "general" password.  ...  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.  ...  Therefore, we try to explore a method to apply deep learning to password guessing in this paper. The password is the main trend [3] for authentication.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/tmm.2019.2940877">doi:10.1109/tmm.2019.2940877</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tqtdn5dxhrghzm6zuzcukum5jm">fatcat:tqtdn5dxhrghzm6zuzcukum5jm</a> </span>
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Machine Learning Use Cases in Cybersecurity

S.М. Avdoshin, A.В. Lazarenko, N.I. Chichileva, P.А. Naumov, P.G. Klyucharev
<span title="">2019</span> <i title="Institute for System Programming of the Russian Academy of Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q5rpshlfgfb5vn5yo6kwvnmsqe" style="color: black;">Proceedings of the Institute for System Programming of RAS</a> </i> &nbsp;
The goal of this paper to explore machine learning usage in cybersecurity and cyberattack and provide a model of machine learning-powered attack.  ...  use cases for implementation and decision making.  ...  There is a new way of generating password guesses based on DL and generative adversarial networks known as PassGAN [42] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15514/ispras-2019-31(5)-15">doi:10.15514/ispras-2019-31(5)-15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dfnfslb4hffvrlmb65gkvrtrsm">fatcat:dfnfslb4hffvrlmb65gkvrtrsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200212095203/https://ispras.ru/proceedings/docs/2019/31/5/isp_31_2019_5_191.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5a/be/5abe20297d9dd22a6672b94da1ee7ff3db181cfb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.15514/ispras-2019-31(5)-15"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Hash Cracking Benchmarking of Replacement Patterns [article]

Ensar Seker
<span title="2020-06-04">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The main purpose of this article is to show that with replacement methods on plain texts, it is possible to have more success rates when trying to recovering hashed passwords.  ...  [17] involved deep learning approach for cracking password patterns for their research.  ...  Their machine learning technique (PassGAN) which can leverage Generative Adversarial Networks (GANs) to enhance password guessing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2006.08839v1">arXiv:2006.08839v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ogudfgpvmncklfditdgqe7hyha">fatcat:ogudfgpvmncklfditdgqe7hyha</a> </span>
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DEEP LEARNING APPLICATIONS IN CYBER SECURITY: A COMPREHENSIVE REVIEW, CHALLENGES AND PROSPECTS

Bhavuk Sharma, Dr Ramchandra Mangrulkar
<span title="2019-12-31">2019</span> <i title="IJEAST"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/namhphsg6rdvtofp27o3scimoy" style="color: black;">International Journal of Engineering Applied Sciences and Technology</a> </i> &nbsp;
Lastly, security is-sues are divided into two sections: attacks on various deep learning net-works, and attacks on a network using Deep Learning models themselves.  ...  This paper surveys deep learning (DL) methods for cyber security applications, highlights the security considerations when using deep learning networks and presents possible malicious uses of such models  ...  Hitaj et al (2019) [67] created PassGAN, a generative adversarial network used for password guessing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.33564/ijeast.2019.v04i08.023">doi:10.33564/ijeast.2019.v04i08.023</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tc2ayamptnc2fehqc6n5trf4fq">fatcat:tc2ayamptnc2fehqc6n5trf4fq</a> </span>
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Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Hojjat Navidan, Parisa Fard Moshiri, Mohammad Nabati, Reza Shahbazian, Seyed Ali Ghorashi, Vahid Shah-Mansouri, David Windridge
<span title="">2021</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/blfmvfslmbggxhopuigjdb3jma" style="color: black;">Computer Networks</a> </i> &nbsp;
approaches.  ...  Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field for the creation of synthetic data through deep generative  ...  PassGAN, based on WGAN-GP, exploits an adversarial network to learn the distribution of real passwords from actual password leaks to generate superior password guesses.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.comnet.2021.108149">doi:10.1016/j.comnet.2021.108149</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ekgil24ijha3evmzruez63tdq">fatcat:4ekgil24ijha3evmzruez63tdq</a> </span>
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Automated Attack and Defense Framework for 5G Security on Physical and Logical Layers [article]

Zhihong Tian, Yanbin Sun, Shen Su, Mohan Li, Xiaojiang Du, Mohsen Guizani
<span title="2019-02-11">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
However, the security of each layer in 5G is mostly studied separately, which causes a lack of comprehensive analysis for security issues across layers.  ...  Correspondingly, traditional security focuses on the core network, and the logical (non-physical) layer is no longer suitable for the 5G network. 5G security presents a tendency to extend from the network  ...  For the password guessing technology, AI-based technologies like PassGAN are used to generate high-quality passwords for password library.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.04009v1">arXiv:1902.04009v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3mq3eypokzgmlpyy57ppms4coa">fatcat:3mq3eypokzgmlpyy57ppms4coa</a> </span>
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Dynamic Markov Model: Password Guessing Using Probability Adjustment Method

Xiaozhou Guo, Yi Liu, Kaijun Tan, Wenyu Mao, Min Jin, Huaxiang Lu
<span title="2021-05-18">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
The model based on enumeration has a lower cover rate for high-probability passwords, and it is a deterministic algorithm that always generates the same passwords in the same order, making it vulnerable  ...  In password guessing, the Markov model is still widely used due to its simple structure and fast inference speed.  ...  PassGAN was introduced in [22] to generate passwords. Then, PassGAN was enhanced [49] to directly learn the probability distribution of the password encoding matrix.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11104607">doi:10.3390/app11104607</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qcyf7tsslrfvfnfpkmcfb5vq3e">fatcat:qcyf7tsslrfvfnfpkmcfb5vq3e</a> </span>
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Machine Learning for Security and the Internet of Things: the Good, the Bad, and the Ugly

Fan Liang, William G. Hatcher, Weixian Liao, Weichao Gao, Wei Yu
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Finally, the most concerning, a growing trend has been the utilization of machine learning in the execution of cyberattacks and intrusions (ugly use).  ...  In this paper, we consider the good, the bad, and the ugly use of machine learning for cybersecurity and CPS/IoT.  ...  Their passGan can autonomously train on leaked passwords from actual systems and then provide high-quality password guesses.  ... 
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CharBot: A Simple and Effective Method for Evading DGA Classifiers [article]

Jonathan Peck, Claire Nie, Raaghavi Sivaguru, Charles Grumer, Femi Olumofin, Bin Yu, Anderson Nascimento, Martine De Cock
<span title="2019-05-30">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
of DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach).  ...  Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time.  ...  Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this  ... 
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