Behavioral biometric traits are not fully distinguished in recognition tasks, but they can improve the overall performance of biometric recognition systems by adding them. The behavioral biometric studied in this paper is related to keystroke dynamic. This paper examines the touch keystroke dynamics of computer users for the purpose of identifying their culture by using four different SVM kernels. It has been confirmed that racial classifications can be made by gathering keystroke data from 250 respondents representing various culture in Malaysia. Results show that different culture categories display different typing patterns. The classification is made using four SVM kernels and a comparison of the accuracy results is shown. The four kernels are Linear, Quadratic, Cubic and Fine Gaussian. The linear kernel has provided the highest accuracy and consistent readings compared to other kernels for the four features evaluated for dynamic keystrokes, namely press-press time, release-release time, press-release time and release-press time. The linear kernel has the highest accuracy reading of 92.4% for classification using press-press time features for the Malay vs Chinese category.

1.
El-Kenawy
,
E.-S.M.
, et al (
2022
),
Meta-Heuristic Optimization and Keystroke Dynamics for Authentication of Smartphone Users
,
Mathematics
,
10
(
16
),
2912
, .
2.
Salem
,
A.
,
A.
Sharieh
, and
R.
Jabri
(
2023
),
Online User Authentication System Using Keystroke Dynamics
,
Journal of Computer Security
,
31
(
3
),
185
215
, .
3.
Vaishnav
,
P.
,
M.
Kaushik
, and
L.
Raja
(
2023
),
Behavioral biometric authentication on smartphone using keystroke dynamics
,
Journal of Discrete Mathematical Sciences & Cryptography
,
26
(
2
),
591
600
, .
4.
González
,
N.
(
2023
),
KSDSLD—A tool for keystroke dynamics synthesis & liveness detection
,
Software Impacts, 15
,
100454
, .
5.
Negaresh
,
F.
,
M.
Kaedi
, and
Z.
Zojaji
(
2023
),
Gender Identification of Mobile Phone Users based on Internet Usage Pattern
,
International Journal of Engineering
,
36
(
2
),
335
347
, .
6.
Marrone
,
S.
and
C.
Sansone
(
2022
),
Identifying Users’ Emotional States through Keystroke Dynamics
,
Deep Learning Theory and Applications
,
207
214
, .
7.
Unni
,
S.
,
S.S.
Gowda
, and
A.F.
Smeaton
,
An Investigation into Keystroke Dynamics and Heart Rate Variability as Indicators of Stress. in
MultiMedia Modeling: 28th International Conference, MMM 2022, Phu Quoc, Vietnam, June 6-10, 2022, Proceedings, Part I. 2022
:
Springer
2022
,
13141
: p.
379
391
, .
8.
Tsimperidis
,
I.
,
C.
Yucel
, and
V.
Katos
(
2021
),
Age and Gender as Cyber Attribution Features in Keystroke Dynamic-Based User Classification Processes
,
Electronics
,
10
(
835
), .
9.
Grunova
,
D.
and
I.
Tsimperidis
(
2023
),
Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics
,
Artificial Intelligence and Data Science for Engineering Improvements
,
4
(
4
),
2711
2721
, .
10.
Oluwatobi
,
A.H.
and O. O.F.W (
2019
),
A Soft Computing Model of Soft Biometric Traits for Gender and Ethnicity Classification
,
International Journal of Engineering and Manufacturing
,
9
(
2
),
54
63
, .
11.
Yaacob
,
M.N.
, et al, Multiple Fusions Approach for Keystroke Dynamics Verification System with Soft Biometrics. in
IOP Conference Series: Materials Science and Engineering
.
2020
:
IOP Publishing 2020
,
917
(
1
): p.
012075
, .
12.
Sari
,
Z.
, et al (
2022
),
Analysis of the Combination of Naïve Bayes and MHR (Mean of Horner’s Rule) for Classification of Keystroke Dynamic Authentication
,
Jurnal Online Informatika
,
7
(
1
),
62
69
, .
13.
S Zeid
,
S.
,
R. A
ElKamar
, and
S. I
Hassan
(
2022
),
Fixed-Text vs. Free-Text Keystroke Dynamics for User Authentication
,
Engineering Research Journal-Faculty of Engineering (Shoubra)
,
51
(
1
),
95
104
, .
14.
Tang
,
C.
, et al (
2022
),
Piezoelectric and Machine Learning Based Keystroke Dynamics For Highly Secure User Authentication
,
IEEE Sensors Journal
,
23
(
20
),
24070
24077
, .
15.
Shojae Chaeikar
,
S.
, et al (
2020
),
PFW: Polygonal Fuzzy Weighted—An SVM Kernel for the Classification of Overlapping Data Groups
,
Electronics
,
0
(
615
),
2
14
, .
16.
Shi
,
Y.
, et al (
2022
),
User authentication method based on keystroke dynamics and mouse dynamics using HDA
,
Multimedia Systems
,
29
(
2
),
653
668
, .
17.
Karnam
,
N.K.
, et al (
2023
),
EMAHA-DB1: A New Upper Limb sEMG Dataset for Classification of Activities of Daily Living
,
EEE Transactions on Instrumentation and Measurement
,
72
,
1
11
, .
18.
Khan
,
M.U.
, et al,
Biometric Authentication System Based on Electrocardiogram (ECG)
. in
2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)
.
2019
:
IEEE
2019: p.
1
6
, .
19.
Khan
,
M.U.
, et al,
Biometric System using PCG Signal Analysis: A New Method of Person Identification
. in
2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)
.
2020
, 2020: p.
1
6
, .
20.
Naser
,
W.
,
E.
Kadim
, and
S.
Abbas
(
2021
),
SVM Kernels comparison for brain tumor diagnosis using MRI
,
Global Journal of Engineering and Technology Advances
,
7
,
026
036
, .
21.
Bhatia
,
A.
and
M.
Hanmandlu
(
2017
),
Keystroke dynamics based authentication using information sets
,
Journal of Modern Physics
,
8
(
9
),
1557
1583
, .
22.
Cardaioli
,
M.
, et al,
Detecting Identity Deception in Online Context: A Practical Approach Based on Keystroke Dynamics
. in
Advances in Human Factors in Cybersecurity: AHFE 2020 Virtual Conference on Human Factors in Cybersecurity, July 16–20, 2020, USA
.
2020
:
Springer
2020,
1219
: p.
41
48
, .
23.
Eude
,
T.
and
C.
Chang
(
2018
),
One-class SVM for biometric authentication by keystroke dynamics for remote evaluation
,
Computational Intelligence
,
34
,
145
160
, .
This content is only available via PDF.
You do not currently have access to this content.