用AI生成数据提升多语言密码强度评估，尤其适配印度用户。
Multilingual AI-Driven Password Strength Estimation with Similarity-Based Detection
- 用非英语数据（印度语料）和ChatGPT生成数据训练密码强度模型。
- 引入基于Jaro相似度的匹配机制，识别与弱密码高度相似的变体。
- 首次针对印度密码设计专用评估器，0.5阈值下匹配准确率达近完美。
随着全球网络攻击事件频发，强化密码安全性至关重要。本文研究了在非英语语料（特别是印度语料）上训练密码强度评估器（PSM）是否可提升性能。结果表明，利用多语言词汇学习能有效改进模型表现。另一贡献是对比分析AI生成数据（以ChatGPT为例）与现有先进模型PassGAN，证明使用AI生成数据的性能更优，暗示PassGAN类工具可能不再必要。为增强检测能力，本文引入基于Jaro相似度的匹配机制，可识别与已知弱密码高度相似的变体，弥补传统直接匹配的不足。最后，首次构建面向印度密码的专用PSM，在Jaro函数值为0.5时达到近乎完美的匹配准确率。尽管受限于数据量和训练规模，结果仍表明采用ChatGPT生成数据是开发安全、语言感知型密码强度评估器的有效策略。
原文摘要 · Abstract (English)
Considering the rise of cyberattacks incidents worldwide, the need to ensure stronger passwords is necessary. Developing a password strength meter (PSM) can help users create stronger passwords when creating an account on an online platform. This research aimed to explore whether incorporating a non-English training dataset (specifically Indian) can improve the performance of a PSM. Findings show that PSMs can be improved by utilising learning of words from other languages. Another contribution of the research was to compare and provide an analysis of AI generated data (specifically by ChatGPT) and PassGAN (existing state-of-the-art model), proving that PassGAN-like tools may no longer be needed as the performance is higher using AI generated data. To further strengthen detection, a Jaro similarity-based matching mechanism was incorporated, enabling the classification of passwords that are highly similar to known weak passwords - this addresses limitations of direct matching techniques used in prior work. A final novel contribution is on developing a PSM tailored for Indian passwords, which has not been developed previously - this resulted in a near-perfect matching accuracy using a Jaro function value of 0.5. Although performance improvements were constrained by limited data and training, results suggest that using the ChatGPT dataset is a viable and effective strategy for developing secure, language-aware password strength meters.
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