Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 11 September 2024 (10:49:15 CEST)
How to cite:
Ghafari, S.; Safari, L.; Afsharchi, M. BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing. Preprints2024, 2024090834. https://doi.org/10.20944/preprints202409.0834.v1
Ghafari, S.; Safari, L.; Afsharchi, M. BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing. Preprints 2024, 2024090834. https://doi.org/10.20944/preprints202409.0834.v1
Ghafari, S.; Safari, L.; Afsharchi, M. BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing. Preprints2024, 2024090834. https://doi.org/10.20944/preprints202409.0834.v1
APA Style
Ghafari, S., Safari, L., & Afsharchi, M. (2024). BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing. Preprints. https://doi.org/10.20944/preprints202409.0834.v1
Chicago/Turabian Style
Ghafari, S., Leila Safari and Mohsen Afsharchi. 2024 "BBPE-AE: A Byte Pair Encoding-Based Auto Encoder for Password Guessing" Preprints. https://doi.org/10.20944/preprints202409.0834.v1
Abstract
In today’s rapidly evolving digital landscape, the significance of password guessing techniques in both offensive and defensive strategies is paramount. Passwords serve as a crucial line of defense for both individuals and corporations, safeguarding their sensitive systems and data. Therefore, assessing the effectiveness of these access credentials is a critical task. However, existing research in this field often encounters limitations, such as a lack of sufficient training data and extended model training times. Current methods often struggle with limited training data and lengthy training times. This paper introduces BBPE-AE, a novel Auto Encoder Network (AE) designed for password guessing. BBPE-AE utilizes Byte-level Byte Pair Encoding (BBPE) to extract frequent tokens from password datasets without length restrictions, employing a dynamic window technique to capture complex patterns. Experimental results on Hotmail and Myspace datasets demonstrate exceptional performance, achieving high similarity rates (BLEU-Unigram: 0.90, BLEU-Bigram: 0.82 for Hotmail; BLEU-Unigram: 0.90, BLEU-Bigram: 0.81 for Myspace). BBPE-AE generates realistic passwords that meet HIBP (Have I Been Pwned) standards, minimizing duplication. These findings highlight the effectiveness of BBPE-AE in enhancing security by generating realistic passwords, ultimately safeguarding sensitive systems and data.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
