One-Class Models for Continuous Authentication Based on Keystroke Dynamics

被引:4
作者
Kazachuk, Maria [1 ]
Kovalchuk, Alexander [1 ]
Mashechkin, Igor [1 ]
Orpanen, Igor [1 ]
Petrovskiy, Mikhail [1 ]
Popov, Ivan [1 ]
Zakliakov, Roman [1 ]
机构
[1] Lomonosov Moscow State Univ, MSU, Dept Comp Sci, Moscow 119899, Russia
来源
INTELLIGENT DATA ENGINEERING AND AUTOMATED LEARNING - IDEAL 2016 | 2016年 / 9937卷
关键词
Keystroke dynamics; User authentication; Kolmogorov-Smirnov test; Quantile discretization; Fuzzy classification; Random forest regression classification;
D O I
10.1007/978-3-319-46257-8_45
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we discuss an applied problem of continuous user authentication based on keystroke dynamics. It is important for a user model to discover new intruders. That means we don't have the keystroke samples of such intruders on the training phase. It leads us to the necessity of using one-class models. In the paper we review some popular feature extraction, preprocessing and one-class classification methods for this problem. We propose a new approach to reduce dimensionality of a feature space based on two-sample Kolmogorov-Smirnov test and investigate how the quantile- based discretization technique can improve the one-class models' performance. We present two algorithms, which have not been used for keystroke dynamics before: Fuzzy kernel-based classifier and Random Forest Regression classifier. We conduct experimental evaluation of the proposed approach.
引用
收藏
页码:416 / 425
页数:10
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