鼠标行为HHT变换的工业互联网用户身份认证

doi:10.11959/j.issn.2096-3750.2022.00268

Abstract

Abstract:

The rapid development of the industrial internet had caused widespread concern about the network security, and the end-user authentication technology was considered a research hotspot.According to the characteristics of human-computer interaction in industrial internet, an experimental website was designed.24 users' mouse behavior data in an uncontrolled environment were collected within 2.5 years to conduct case studies.Hilbert-Huang transform (HHT) was used to extract frequency domain features of mouse behavior signals, combined with time domain features to form a time-frequency joint domain feature matrix of 163-dimensional to characterize user mouse behavior patterns.Bagged tree, support vector machine (SVM), Boost tree and K-nearest neighbor (KNN) were used to build a user authentication model， and the comparison result showed that the Bagged tree had the best internal detection effect in this case, with an average false acceptance rate (FAR) of 0.12% and an average false rejection rate (FRR) of 0.28%.In external detection, the FAR was 1.47%.Compared with the traditional mouse dynamics method, the frequency domain information of mouse behavior extracted by HHT can better realize the user authentication, and provide technical support the security of the industrial internet.

Key words: industrial internet, identity authentication, mouse behavior, Hilbert-Huang transform, Bagged tree

CLC Number:

TP273

Yigong ZHANG, Qian YI, Jian LI, Congbo LI, Aijun YIN, Shuping YI. User authentication of industrial internet based on HHT transform of mouse behavior[J]. Chinese Journal on Internet of Things, 2022, 6(2): 77-87.

Figures/Tables 10

时域特征	计算方法
X轴坐标	X
Y轴坐标	Y
X轴速度	$v_{x} = δ x / δ t$
Y轴速度	$v_{y} = δ y / δ t$
切向速度	$v = \sqrt{v_{x}^{2} + v_{y}^{2}}$
切向加速度	$a = δ v / δ t$
速度二阶导数	$j = δ a / δ t$
速度三阶导数	$s = δ j / δ t$
速度四阶导数	$d = δ s / δ t$
角度	$θ = \arctan (δ y / δ x)$
角度变化率	$v_{θ} = δ θ / δ t$
曲率	$c = δ θ / δ s$
曲率变化率	$v_{cur} = δ c / δ t$
曲率距离^[11]	cd

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鼠标事件	原始数据中的名称	记号
鼠标移动	slide	m_t
按键按下	left down/right down	d_t
按键弹起	left up/right up	u_t

类型			特征名称			特征数
	v_x
	v_x
	v				最小值
	a		特征本身		最大值
与时间无关	v′′	×	瞬时频率	×	平均值	135
	v′′′		瞬时能量		标准差
	v′′′′				极差
	v_θ
	v_cur
	X		最小值
	Y		最大值
与时间无关	θ	×	平均值			25
	c		标准差
	cd		极差
	s_n
其他	t_n					3
	J

真实用户	FAR	FRR
A	0.05%	0.65%
B	0.18%	0.13%
C	0.09%	0.06%
D	0%	0%
E	0.15%	0.26%
F	0.25%	0.19%
G	0%	0.34%
H	0.06%	0.15%
I	0.09%	0.39%
J	0.24%	0.33%
K	0.36%	0.71%
L	0.08%	0.24%
M	0.06%	0.56%
N	0.03%	0.09%
O	0.06%	0.36%
P	0%	0.03%
Q	0.19%	0.22%
R	0.22%	0.26%
平均	0.12%	0.28%

外部入侵者	样本数量	FAR
入侵者1	470	0.72%
入侵者2	49	1.53%
入侵者3	124	1.41%
入侵者4	97	2.57%
入侵者5	162	1.77%
入侵者6	101	0.80%
平均	167.17	1.47%

分类器	FAR	FRR
Bagged tree（本文）	0.12%	0.28%
Boost trees	0.29%	0.64%
高斯核SVM	0.38%	0.89%
KNN	0.66%	1.83%

User authentication of industrial internet based on HHT transform of mouse behavior

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研究	方法	实验设计	结果
Ahmed等^[9]	MD-NN	22名被试者，短期非受控环境	FAR=2.464 9%
			FRR=2.461 4%
Gamboa等^[34]	Weibull分布	50名被试者，15 min短期受控游戏环境	EER=0.5%
Shen等^[35]	SVM（单核）	28名被试者，30 min短期非受控环境	FAR=0.37%
			FRR=1.12%
Feher等^[10]	MD	25名被试者，短期受控环境	EER=10%
Zheng等^[36]	MD-SVM	14名被试者，短期受控环境	FAR=2.96%
			FRR=0.86%
本文（内部检测）	HHT-BT	4名被试者，超过两年的长期非受控环境	FAR=0.12%
			FRR=0.28%
本文（外部检测）			FAR=1.47%

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