基于跨域功能增强的密码猜测方法用于小样本
Cheng Liu1,2,3, Junrong Li4, Xiheng Liu4
1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Entropy (Basel, Switzerland)
|July 29, 2025
概括
本研究介绍了一种新的小样本密码猜测技术,使用概率无上下文语法 (PCFG) 来克服数据限制. 该方法增强了跨域的功能,提高了密码猜测精度高达10.52%.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 信息安全 信息安全
背景情况:
- 猜测密码对于帐户保护和入侵检测至关重要.
- 传统模型需要大量的数据集,但隐私法规限制了数据访问.
- 这对研究人员来说是一个挑战,他们需要从小组中猜测密码.
研究的目的:
- 开发一个小样本的密码猜测技术,增强跨领域的功能.
- 解决隐私受限制的环境中传统模式的局限性.
- 用有限的数据提高密码猜测的效率和准确性.
主要方法:
- 使用概率上下文自由语法 (PCFG) 分析密码集,以导出结构和碎片概率.
- 生成密码集结构向量用于使用等号相似性进行相似性比较.
- 通过修改训练集结构向量,增强了小样本密码集功能.
主要成果:
- 小和大密码集之间的相似度测量对于超过150个样本的集是可靠的.
- 泄露和目标密码集之间的更高相似性与增加的命中率有关.
- 拟议的特征增强方法提高了小样本集的命中率,高达10.52%.
结论:
- 开发的技术有效地解决了小样本密码猜测的挑战.
- 它提供了一个可行的解决方案,而不需要对目标密码集的预先了解.
- 该方法在数据有限的场景中增强了安全研究能力.
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