Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electronic and Information Engineering | en_US |
| dc.contributor.advisor | Hu, Haibo (EIE) | en_US |
| dc.creator | Fan, Jiye | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/12048 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Smart password brute forcing using deep neural networks | en_US |
| dcterms.abstract | This dissertation explores deep neural networks that can be used in password brute-forcing cracking. To reinforce past research works in the field of password brute-forcing, the dissertation designs a deep neural network model called PassBF. The first part of PassBF is based on the IWGAN model and trained on the first password dataset, whereas the second part adopts transfer learning to transfer the IWGAN model to a second password dataset. In the process of transfer learning, the generator model is fixed while the discriminator model is trained with the second dataset, the discriminator model is fixed while the generator model is trained with the second dataset. Through the three-phase training, the feature of the first dataset is transferred to the second one, and the deep neural network is able to distinguish passwords and generate passwords similar to the second dataset. Then through experiments, the dissertation explores the effect of the transfer learning rate as well as other training parameters of the deep neural network. Based on two popular password training datasets, namely, the Reddit Username dataset and the RockYou password dataset. The dissertation also compares the performance of the PassBF model with the PassGAN model, which is a deep neural network used for password brute-forcing, on some common password brute-forcing tools. The comparison result shows the passBF has sufficient potent in the password brute-forcing field. | en_US |
| dcterms.extent | 63 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2022 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.LCSH | Computer security | en_US |
| dcterms.LCSH | Machine learning | en_US |
| dcterms.LCSH | Neural networks (Computer science) | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6512.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.45 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/12048