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基于GPT-2模型的姓氏口令猜测方法

林嘉熹 钱秋妍 曾剑平 张尉东

计算机科学与探索2025,Vol.19Issue(4):1087-1094,8.
计算机科学与探索2025,Vol.19Issue(4):1087-1094,8.DOI:10.3778/j.issn.1673-9418.2407028

基于GPT-2模型的姓氏口令猜测方法

Surname Password Guessing Method Based on GPT-2

林嘉熹 1钱秋妍 1曾剑平 1张尉东2

作者信息

  • 1. 复旦大学 计算机科学技术学院,上海 200433||教育部网络信息安全审计与监控工程研究中心,上海 200433
  • 2. 上海壁仞科技股份有限公司,上海 201100
  • 折叠

摘要

Abstract

As authentication mechanisms diversify,passwords,as a traditional and widely adopted authentication method,face severe security challenges.Due to linguistic characteristics and cultural differences,Chinese users'password choices differ significantly from those of English-speaking users,providing new perspectives for guessability attacks.To address this issue,this paper proposes a Chinese surname-based password guessing method using the GPT-2 model,aiming to effectively enhance the guessing capability for Chinese passwords.The proposed method employs unsupervised fine-tuning to enable the pre-trained language model to generate passwords closely related to surnames.To compensate for GPT-2's lack of support for Chinese characters,this model leverages a news corpus as the pre-training dataset,converting Chinese text into Pinyin and training the model to recognize Pinyin,thereby helping the model more accurately understand Chinese users'password habits.Experimental results demonstrate that the proposed model exhibits superior performance in password guessing tasks,particularly in resource-constrained environments,achieving higher success rates compared with traditional guessing methods and deep learning-based password attack techniques.Additionally,this paper explores the impact of temperature parameters on the success rate of password guessing,identifying potential directions for further improving password security.

关键词

口令安全/中文口令/GPT-2模型/口令猜测/预训练语言模型

Key words

password security/Chinese password/GPT-2 model/password guessing/pre-trained language model

分类

信息技术与安全科学

引用本文复制引用

林嘉熹,钱秋妍,曾剑平,张尉东..基于GPT-2模型的姓氏口令猜测方法[J].计算机科学与探索,2025,19(4):1087-1094,8.

基金项目

复旦大学-壁仞科技联合实验室项目(202201).This work was supported by Fudan University-Biren Technology Joint Laboratory Project(202201). (202201)

计算机科学与探索

OA北大核心

1673-9418

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