Enhancing Password Guessing with Pattern-Guided Generative Transformer and Duplicate Reduction

Kernkonzepte

PagPassGPT, a password guessing model built on Generative Pretrained Transformer (GPT), can perform pattern guided guessing by incorporating pattern structure information as background knowledge, resulting in a significant increase in the hit rate. Furthermore, the proposed D&C-GEN algorithm reduces the repeat rate of generated passwords by recursively dividing the guessing task into non-overlapping subtasks.

Zusammenfassung

The paper presents PagPassGPT, a password guessing model constructed on Generative Pretrained Transformer (GPT), and an enhancement algorithm called D&C-GEN to address the challenges in existing deep learning-based password guessing models.
Key highlights:

PagPassGPT integrates password patterns as background knowledge during model predictions, enabling effective pattern guided password guessing. It outperforms the state-of-the-art PassGPT model by up to 27.5% in hit rate for pattern guided guessing.
D&C-GEN adopts a divide-and-conquer approach to recursively divide the password guessing task into non-overlapping subtasks. This reduces the repeat rate of generated passwords, achieving only 9.28% repeat rate at 10^9 guesses compared to 34.5% for PassGPT.
Comprehensive experiments on public datasets demonstrate the superior performance of the proposed schemes in terms of hit rate and repeat rate, both in trawling attacks and cross-site attacks.

Statistiken

The top 10 password patterns are consistent across all datasets and align with those observed within individual datasets.
When the number of guesses reaches 10^9, the repeat rate of the proposed scheme is only 9.28%, while PassGPT reaches 34.5%.
PagPassGPT achieves up to approximately 27.5% improvement in hit rate during the test of pattern guided guessing compared to PassGPT.
In the trawling attack test, PagPassGPT-D&C (with D&C-GEN) achieves a hit rate of 53.63% at 10^9 guesses, approximately 12% higher than PassGPT.

Zitate

"PagPassGPT successfully achieves our goal of effectively generating passwords in a pattern guided guessing manner while also leveraging the power of GPT-2."
"D&C-GEN adopts the concept of a divide-and-conquer approach, recursively dividing the main guessing task into small, non-overlapping subtasks with distinct requirements, including different patterns and different prefixes."

Wichtige Erkenntnisse aus

PagPassGPT

by Xing... um arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.04886.pdf

Tiefere Fragen

How can the proposed techniques be extended to other text generation tasks beyond password guessing?

The techniques proposed in PagPassGPT, such as pattern guided guessing and the use of Generative Pretrained Transformer (GPT), can be extended to various other text generation tasks beyond password guessing. One way to extend these techniques is to apply them in the field of natural language processing (NLP) for tasks like text summarization, language translation, and dialogue generation. By incorporating specific patterns or structures into the generation process, models can be guided to produce more coherent and contextually relevant text. Additionally, leveraging pretrained transformer models like GPT can enhance the quality and fluency of generated text in various NLP applications. Fine-tuning these models on specific datasets related to the target task can further improve their performance and adaptability to different text generation tasks.

What are the potential privacy and security implications of highly effective password guessing models, and how can they be mitigated?

Highly effective password guessing models pose significant privacy and security implications, as they can potentially compromise user accounts and sensitive information. Some of the key implications include an increased risk of unauthorized access to personal data, financial accounts, and confidential information. To mitigate these risks, several strategies can be implemented:

Stronger Password Policies: Encouraging users to create complex and unique passwords can make it harder for guessing models to crack them.
Multi-Factor Authentication: Implementing multi-factor authentication adds an extra layer of security, even if passwords are compromised.
Regular Password Updates: Encouraging users to update their passwords regularly can reduce the impact of compromised passwords.
Monitoring and Detection: Implementing systems to monitor and detect unusual login attempts can help identify potential password guessing attacks.
Limiting Login Attempts: Implementing rate limiting on login attempts can prevent brute force attacks and reduce the effectiveness of password guessing models.

What other types of background knowledge, beyond password patterns, could be incorporated to further enhance the performance of password guessing models?

In addition to password patterns, there are several other types of background knowledge that could be incorporated to enhance the performance of password guessing models:

User Behavior: Analyzing user behavior patterns, such as typing speed, frequency of password changes, and common login times, can provide valuable insights for generating more accurate guesses.
Geolocation Data: Incorporating geolocation data to understand where users typically log in from can help tailor password guessing strategies based on location.
Common Phrases and Keywords: Considering common phrases, keywords, or themes that users often use in their passwords can improve the model's ability to generate relevant guesses.
Password Strength Metrics: Utilizing password strength metrics to guide the generation process towards creating more secure and complex passwords.
Contextual Information: Incorporating contextual information related to the user's account, such as account creation date, account activity, or account preferences, can further refine the guessing process and generate more targeted guesses.