基于上下文语义的口令攻击模型

PASSWORD ATTACK MODEL BASED ON CONTEXT SEMANTIC

  • 摘要: 大数据技术给口令安全研究带来新的思路,通过该技术已经发现了口令中存在的姓氏、常用词汇、常用数字串等典型语义,但口令中仍存在大量无法归类的语义特征,限制了口令猜测成功率。提出一种基于上下文语义的口令建模方法,具体包括在口令切分中减小无效语义、建立未知语义的长度分布、在上下文无关模型中引入未知语义三个主要处理过程。在大规模口令数据集上的攻击实验表明,所提语义口令建模方法对于现有口令猜测模型的成功率有一定提升。

     

    Abstract: Bigdata technology brings new approach for password security analysis, so that typical semantics such as surname, commonly-used vocabulary and commonly-used number string have been found in passwords. However, there are still a large number of semantic features that cannot be categorized, which limits the success rate of password guessing. A password modeling method based on context semantics is proposed, which includes three main processing processes: reducing invalid semantics in password segmentation, establishing length distribution of unknown semantics, and introducing unknown semantics to context-free model. Attack experiments were carried out on a large password dataset CSDN. The results show that the proposed semantic password modeling method can improve the efficiency of the existing password guessing model to some extent.

     

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