基于顽健线性判别分析的击键特征识别方法

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

通信学报 ›› 2017, Vol. 38 ›› Issue (Z2): 26-29.doi: 10.11959/j.issn.1000-436x.2017272

基于顽健线性判别分析的击键特征识别方法

沈伟国^1,²,王巍^1,²

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

出版日期:2017-11-01 发布日期:2018-06-07
作者简介:沈伟国（1987-），男，浙江湖州人，中国电子科技集团公司第三十六研究所工程师，主要研究方向为网络安全。|王巍（1980-），男，河北张家口人，博士，中国电子科技集团公司第三十六研究所高级工程师，主要研究方向为网络安全。

Keystroke features recognition based on stable linear discriminant analysis

Wei-guo SHEN^1,²,Wei WANG^1,²

¹ Science and Technology on Communication Information Security Control Laboratory,Jiaxing 314033,China
² No.36 Research Institute of CETC,Jiaxing 314033,China

Online:2017-11-01 Published:2018-06-07

摘要/Abstract

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。

关键词: 身份认证, 击键序列识别, 特征提取, 顽健线性判别分析

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.

Key words: identity authentication, keystroke recognition, feature extraction, stable linear discriminant analysis

中图分类号:

TP391.4

沈伟国,王巍. 基于顽健线性判别分析的击键特征识别方法[J]. 通信学报, 2017, 38(Z2): 26-29.

Wei-guo SHEN,Wei WANG. Keystroke features recognition based on stable linear discriminant analysis[J]. Journal on Communications, 2017, 38(Z2): 26-29.

图/表 4

参考文献 15

[1]	GAINES R , LISOWSKI W , PRESS S . Authentication by keystroke timing:some preliminary results[R]. Rand Corporation:Rand Report R-2560-NSF, 1980.
[2]	JOYCE R , GUPTA G . Identity authentication based on keystroke latencies[J]. Commun ACM, 1990,33(2): 168-176.
[3]	ROBINSON J , LIANG V . Computer user verification login string keystroke dynamics[J]. IEEE Trans Syst Man Cybern, 1998,28(2): 236-241.
[4]	LEGGETT J , WILLIAMS G , USNICK J . Dynamic identity verification via keystroke characteristics[J]. International Journal of Man-machine Studies, 1991,(35): 859-870.
[5]	NAPIER R , LABERTY W , MAHAR W . Keyboard user verification:toward an accurate,efficient,andecologically valid algorithm[J]. International Journal of Human Computer Studies, 1995(43): 213-222.
[6]	DUNN A . biometric authentication-real identities for a virtual world,MIDAS[R]. Project Development Final Report, 2002.
[7]	SALEH B , CHARLES S , BASSAM E . Computer-access security systems using keystroke dynamics[J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1990,12(12): 1217-1222.
[8]	高艳, 管晓宏, 冯力 . 基于实时击键序列的主机入侵检测[J]. 计算机学报, 2004,27(3): 396-401.
	GAO Y , GUAN X H , FENG L . The host-based intrusion detection based on real time keystroke sequences[J]. Chinese Journal of Computers, 2004,27(3): 396-401.
[9]	MONROSE F , RUBIN A . Authentication via keystroke dynamics[C]// Fourth ACM Conference on Computer and Communications Security, 19973.
[10]	SHI Y , CAO L M . User identity verification based on recognition of typing style[J]. Computer Engineering, 2005,31(6).
[11]	LIANG J , WANG X , CHEN W W . Recognition of user’s keystroke features based on difference subspace[J]. Computer Engineering, 2007,33(11)
[12]	WANG Y J , ZHAO P H , WANG M M . Method of user identification based on keystroke behavior and its application[J]. Computer Science, 2015,42(11).
[13]	王振辉, 王振铎, 支侃买 . 基于鼠标和键盘行为特征组合的用户身份认证[J]. 计算机应用与软件, 2016,33(7): 308-312.
	WANG Z H , WANG Z D , ZHI K M . User identity authentication based on mouse and keyboard behavioural biometrics combination[J]. Computer Applications and Software, 2016,33(7): 308-312.
[14]	王晅, 陈伟伟, 马建峰 . 基于遗传算法和灰色关联分析的击键特征识别算法[J]. 计算机应用, 2007,27(5): 1054-1057.
	WANG X , CHEN W W , MA J F . User authentication algorithm with keystroke features based on genetic algorithms and grey relational analysis[J]. Journal of Computer Applications, 2007,27(5): 1054-1057.
[15]	蒋李芬, 刘家芬 . 基于键盘行为数据的用户身份识别[J]. 计算机应用, 2015,35(SI): 110-112.
	JIANG L F , LIU J F . User authentication based on keystroke dynamics[J]. Journal of Computer Applications, 2015,35(SI): 110-112.

基于顽健线性判别分析的击键特征识别方法

Keystroke features recognition based on stable linear discriminant analysis

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 4

参考文献 15

相关文章 15

Metrics

推荐阅读 0

[1]	江沸菠, 彭于波, 董莉. 面向6G的深度图像语义通信模型[J]. 通信学报, 2023, 44(3): 198-208.
[2]	兰巨龙, 朱棣, 李丹. 面向多模态网络业务切片的虚拟网络功能资源容量智能预测方法[J]. 通信学报, 2022, 43(6): 143-155.
[3]	王晓丹, 李京泰, 宋亚飞. DDAC：面向卷积神经网络图像隐写分析模型的特征提取方法[J]. 通信学报, 2022, 43(5): 68-81.
[4]	来杰, 王晓丹, 向前, 宋亚飞, 权文. 自编码器及其应用综述[J]. 通信学报, 2021, 42(9): 218-230.
[5]	唐飞, 包佳立, 黄永洪, 黄东, 王惠莅. 基于属性的多授权中心身份认证方案[J]. 通信学报, 2021, 42(3): 220-228.
[6]	王后珍, 蔡鑫伟, 郭岩, 张焕国. 基于矩阵填充问题的五轮零知识身份认证方案[J]. 通信学报, 2021, 42(11): 79-86.
[7]	肖利民,徐向荣,韦壮焜,刘圣涵,刘怡文. 基于信道冲激响应不敏感特征的分子通信非相干信号检测[J]. 通信学报, 2020, 41(9): 49-58.
[8]	顾纯祥,吴伟森,石雅男,李光松. 基于自编码器的未知协议分类方法[J]. 通信学报, 2020, 41(6): 88-97.
[9]	屈景怡,叶萌,渠星. 基于区域残差和LSTM网络的机场延误预测模型[J]. 通信学报, 2019, 40(4): 149-159.
[10]	朱智强,林韧昊,胡翠云. 基于数字证书的openstack身份认证协议[J]. 通信学报, 2019, 40(2): 188-196.
[11]	祝烈煌,王龙,李嘉盛,张川,原卫华. 卫星通信网中一种新的实体认证与访问控制方案[J]. 通信学报, 2018, 39(6): 73-80.
[12]	赵森,甘庆晴,王晓明,余芳. 多云环境下基于智能卡的认证方案[J]. 通信学报, 2018, 39(4): 131-138.
[13]	张佳乐,赵彦超,陈兵,胡峰,朱琨. 边缘计算数据安全与隐私保护研究综述[J]. 通信学报, 2018, 39(3): 1-21.
[14]	盖杉. 四元共空间特征提取算法及其在纸币识别中的应用[J]. 通信学报, 2018, 39(12): 40-46.
[15]	李华亮,钱志鸿,田洪亮. 基于核函数特征提取的室内定位算法研究[J]. 通信学报, 2017, 38(1): 158-167.