Keystroke features recognition based on stable linear discriminant analysis

Wei-guo SHEN; Wei WANG

doi:10.11959/j.issn.1000-436x.2017272

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Keystroke features recognition based on stable linear discriminant analysis

Papers | 更新时间：2024-06-05

- Keystroke features recognition based on stable linear discriminant analysis
- Journal on Communications Vol. 38, Issue Z2, Pages: 26-29(2017)
- 作者机构：
  
  1. 通信信息控制和安全技术重点实验室，浙江嘉兴 314033
  2. 中国电子科技集团公司第三十六研究所，浙江嘉兴 314033
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2017272
  CLC： TP391.4
- Online First：2017-11，
  
  Published：25 November 2017
- 稿件说明：
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Wei-guo SHEN, Wei WANG. Keystroke features recognition based on stable linear discriminant analysis[J]. Journal on Communications, 2017, 38(Z2): 26-29.
DOI：

Wei-guo SHEN, Wei WANG. Keystroke features recognition based on stable linear discriminant analysis[J]. Journal on Communications, 2017, 38(Z2): 26-29. DOI： 10.11959/j.issn.1000-436x.2017272.

摘要

研究了用户认证过程中的键盘击键序列特征提取和分类问题，提出一种基于顽健线性判别分析的击键特征识别方法。首先，最大化击键序列集不同类间的离散度，同时最小化序列集同类之间的离散度，保持击键序列样本的最佳判别特性。其次，最小化近邻击键序列样本间的相似性离散度，保持序列样本的区域相似性。最后，基于上述原则，对击键序列特征样本进行特征提取，并采用最近邻分类准则进行判决输出。通过与其他方法的实验对比，验证了该方法的有效性。

Abstract

A novel keystroke features recognition method based on stable linear discriminant Analysis (SLDA) was put forward.First of all

it maximum the dispersion between different sequences

while minimizing the dispersion between the same sequence set

maintain the best discriminant characteristics of the keystroke sequences.Secondly

the local similarity graph between keystroke sequences is constructed

minimizing the dispersion of the local similarity sequences

to keep the local similarity of keystroke sequences.Finally

based on the principles above

the feature of keystroke sequences are extracted

and the nearest neighbor classification criterion is used to judge the outputs.The effectiveness of the proposed method is certified by experiment results.

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references

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