Article Publishing History Article Received on : 30 Dec 2022 Article Accepted on : 01 Mar 2023 Article Published : 03 Mar 2023 Plagiarism Check: Yes Reviewed by: Dr. Anil V. Turukmane Second Review by: Dr. R.N. Devendra Kumar Final Approval by: Dr. Lim Eng Aik
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ABSTRACT:
System security is very important, especially in the age that we live in. One of the ways to secure data is by creating a password that makes it difficult for unauthorized user to gain access to the system. However, what makes it difficult for the system to be attacked is directly dependent on approach used to create it, and how secured it is. Text based approach is the oldest authentication approach. It requires that the user supplies textual password in order to gain access to the system. However, this approach has shown a significant drawback and several vulnerabilities, one of which is the difficulty in recalling or remembering textual passwords. Several other attacks that textual passwords are vulnerable to include brute force attacks, shoulder spying, dictionary attacks etc. The introduction of graphical schemes made things a lot better. Graphical passwords make use of images. However, most graphical schemes are vulnerable to shoulder surfing attacks. In this research work, we developed two systems; A position-based multi-layer graphical user authentication system and an Image-based multi-layer graphical user authentication system. The reason behind this research work is to compare the two systems, and evaluate them based on three major performance metrics: (1) Security, (2) Reliability (3) Individual preference.
Edward A. L, Suru H, Giro M. A. A Comparison between Position-Based and Image-Based Multi-Layer Graphical User Authentication System. Orient.J. Comp. Sci. and Technol; 16(1).
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Edward A. L, Suru H, Giro M. A. A Comparison between Position-Based and Image-Based Multi-Layer Graphical User Authentication System. Orient.J. Comp. Sci. and Technol; 16(1). Available from: https://bit.ly/3y5X5MI
Introduction
The heart of security system is user authentication.
When it comes to computer system security, Human
factors are often considered the weakest link.
There are three major areas where human computer interaction is
important: authentication, security operations, and developing secure systems
(Patrick, et al). Here we focus on the authentication problem. The most common
computer authentication method is for a user to submit a user name and a text password.
The challenge with this approach is that it is difficult to remember long
passwords, and so users prefer to use short passwords, which can be easily
guessed or stolen.
Graphical user authentication scheme was
introduced as an alternative to text-based schemes, which was somehow motivated
by the fact that humans can easily remember pictures better than text;
psychological studies supports this assumption as well. Pictures are generally
easier to be remembered or recognized than text (R. N. Shepard).
Seeing
that, most graphical Passwords schemes are prone to shoulder surfing and
malware attacks (Vimal et al., 2017). We embarked on this reaserch work to
develop two graphical user authentication schemes, and compare both of them,
order to test against shoulder surfing attack. The first scheme is image-based,
in which the images selected during registration becomes the user passward,
while the second scheme is position-based, where the user only pays attention
to the position of the images at the point of registration, keeps the positions
to heart, as those positions will become user password.
Related work
So
many related projects which captures the minds and thoughts of scholars and
researchers that have worked on areas relating to this subject matter were
reviewed. Intelligent and useful scientific techniques was used to develop
schemes in a bid to help provide security to personal information of users and
prevent attacks. Some of these research works are given below:
Tunga (2015), presented a survey of
comparative study between different techniques of Graphical User Authentication
(GVA). GUA has been considered to be a better alternative to text-based
authentication, because psychologists have been able to prove, that humans
remember images better than text. The
strengths of each Graphical User Authentication technique were listed out, and
their unique features, alongside the weaknesses. kaka et. al (2021) reviewed 10 recognition based graphical
passwords algorithms, and evaluated them which respect to their individual
strength and weaknesses and also analyzed them on the basis of their common
usability and security threats. A comparison table was shown which showed that
shoulder surfing attack remains a challenge for graphical password
authentication. Even though, researchers have been able to develop algorithms
to solve this problem, users still find it hard to easily create and understand
recognition based graphical passwords. In a research work carried out by Katsini et al, (2019), an eye tracking study was
done in a bid to investigate the effects of users’ cognitive styles towards the
strength of the password that the user created and also explain whether and how
the visual strategy during the graphical password composition, directly
influences the passwords’ strength. Witkin’s Field Dependence-Independence
Theory was adopted, and the analysis showed that users with different cognitive
processing Characteristics, followed different patterns of visual behavior when
they were creating their password, and this affected the strength of the
password they created. Ndako et al., (2021)
took a closer look at Pure Recall-based GUAs with emphasis on the contextual
parameter used for user authentication. It also opens up all the Pure
Recall-based graphical user authentication schemes that were developed in the
first 20 years (1996-2016) that Graphical passwords were introduced and the
recently developed schemes. These studies were carried out in a bid to come up
with a better positioned Pure Recall-Based Graphical User Authentication
schemes, as alternatives to text password. Istyaq
et al, (2021) proposed a security system which Combines both textual and
graphical password, and uses the generation of Unique Grid Code (UGC), which is
been selected by a user during registration, and then becomes the user’s
password. The significant feature that makes the security level of the proposed
system quiet potent is that the system assigns a unique code for each image
that is been selected, will varies from one image to another. Users are to
select not more than 10 images and make not more than 5 clicks on each image.Atish, (2016)
used persuasive Cued Click Points to influence the choice of users in
click-based graphical passwords, in a bid to encourage users to select more
images, so that it would be very difficult for hackers to guess the
clicked-points. The main focus of the work, was on the evaluation of the
Persuasive Cued points (PCP) graphical authentication system which incorporates
usability and system security in three different levels. Furthermore, Suru and Murano, (2019), gave a detailed review of the
current state of research in graphical authentication system. It also gives
concise description of some of the mechanisms used in graphical authentication
along with the strength and flaws of each. Some of the flaws include
predictability, difficulty involved in using the system, its vulnerability to
attacks, and the inability of systems to combine security and usability
efficiently. The paper concluded with suggestions for possible improvements of
each authentication system. Bhand et al., (2015)
came up with a scheme that would be easy to use, give higher security so that
it would be very difficult for attackers to gain access to the system. In this
papers, cued click point (CCP) being the best and more reliable alternative for
text password and the old graphical password system, was combined with new
technologies like mobile phones and E-mail. The system was examined using 500
images of the same format. The result showed that the system is not prone to
Brute force attack and is secured, as an alert message when an attacker tries
to login with incorrect details after the third attempt. A few researchers designed and implemented a
polynomial based Google Map Graphical Password (P-GMGP) system. This is an
improvement of the existing Google Map Graphical Password system in which a
specific location serves as password for authentication, so and that location
can be captured by an attacker. The proposed system is resistant to shoulder
Surfing attack and is faster than the existing system. It also allows efficient
and effective user authentication in cloud environment (Zhou et al 2019). Wang,
(2020) took a study and reviewed the existing systems and saw a gross
limitation of computational resources for mobile nodes. Hence, a great need for
the development of a light weight anonymous and antiquantum scheme for
authentication, so that mobile nodes can roam securely on multiple service
domain. A new scheme was developed, which when compared with the existing
scheme showed great improvement in terms of efficiency, system security and
resistance to quantum attack.
Methodology
The
methodology adopted for this research work is the Design Research methodology
(DRM). This method was carefully selected because it supports a more rigorous
research approach by helping to plan and implement design research. Research
methodology also shows how the research outcome at the end will be obtained in
line with meeting the objective of the study (Sileyew, 2019).
In
this system, several modules are integrated and combined to interact with
themselves to provide the functionalities of the system. The basic modules of
the system are:
Registration Module
This module allows new users to create an account with the system by registering in the registration page.
Login module
This module allows users and admin to access the system by entering their login details. It also creates a session for each login by the user.
Home Module: This module presents all the activities carried out by the system.
Logout module: this module terminates a user’s session and allows them to exit the system.
Results and Discussion
The two software (Position-based Multi-Layer
Graphical User Authentication System and Image-based Multi-Layer Graphical User
Authentication System) were implemented using the following tools.
Laptop
Django Server
PostgreSQL
PG Admin4
Brackets & Visual Studio Code
HTML5, CSS3, JavaScript
Google Chrome
Activity diagram
This is a model of processes in the system. It offers control flow and data flow mechanisms that coordinate the processes in the system. The activity diagram is illustrated below.
After
registration, the user will need to go through three different phases to login.
Each Login phase is connected to the next. So, if a user supplies a wrong login
details in the 1st phase, he would not be able to move to the 2nd
phase. The user successfully login to the system after passing through the
three authentication phases.
Performance Evaluation
In
order to properly carry out performance evaluation on the system, we compared
the Image-based graphical user authentication system with the Position-based
Multi-layer graphical user authentication system. The performance metrics we
used are:
Security
Reliability
Individual Preference
The
approach we used for this experiment is the within user, in which the total
number of users were been divided into two groups. Some of the users begin by
using the first system while others begin by using the second system, after
which the users will swap. At the end of the day, all the users we able to use
both systems. We used a total of 50
participants for this experiment. Each user registered and logged in using the
systems. Their registration and login time was recorded and their comments were
received using google form and then interpreted and analyzed using SPSS
(Statistical Package for Social Sciences). A summary of their registration and
login time is shown in the tables below:
Table 1: Registration time of Users (Position-Based GUAS)
Users
Registration Time
Percentage (%)
18 users
1- 60 seconds
36%
26 users
60-120 seconds
52%
6 users
120-200 seconds
12%
Table 2: Calculation of mean for Registration time of Users (Position-Based GUAS)
Registration Time
Average time (x)
Users (f)
Fx
1- 60 seconds
30 seconds
18 users
540
60-120 seconds
60 seconds
26 users
1560
120-200 seconds
90 seconds
6 users
540
∑f=50
∑fx=2640
Table 3: Registration time of Users (Image-Based GUAS)
Users
Registration Time
Percentage (%)
12 users
1- 60 seconds
24%
23 users
60-120 seconds
46%
15 users
120-200 seconds
30%
Table 4: Calculation of mean for Registration time of Users (Image -Based GUAS)
Registration Time
Average time (x)
Users (f)
Fx
1- 60 seconds
30 seconds
12 users
360
60-120 seconds
60 seconds
23 users
2070
120-200 seconds
90 seconds
15 users
1350
∑f=50
∑fx=3780
From the mean gotten from table 4.2, the average registration time of
users for the (Position-Based GUAS) is 52.8 seconds. But, from the mean gotten
from table 4.4, the average registration time for (Image-Based GUAS) is 75.6
seconds. Hence, the Position-Based GUAS takes shorter time to register.
Table 5: Login time of Users (Position-Based GUAS)
Users
Login Time
Percentage (%)
36 users
1- 60 seconds
72%
12 users
60-120 seconds
24%
2 users
120-200 seconds
4%
Table 6: Calculation of mean for Login time of Users (Position -Based GUAS)
Login Time
Average time (x)
Users (f)
Fx
1- 60 seconds
30 seconds
36 users
1080
60-120 seconds
60 seconds
12 users
720
120-200 seconds
90 seconds
2 users
180
∑f=50
∑fx=1980
Table 7: Login time of Users (Image-Based GUAS)
Users
Login Time
Percentage (%)
36 users
1- 60 seconds
72%
10 users
60-120 seconds
20%
4 users
120-200 seconds
8%
Table 8: Calculation of mean for Login time of Users (Image -Based GUAS)
Login Time
Average time (x)
Users (f)
Fx
1- 60 seconds
30 seconds
36 users
1080
60-120 seconds
60 seconds
10 users
600
120-200 seconds
90 seconds
4 users
360
∑f=50
∑fx=2040
The result of the mean gotten from table 4.6, the average login time of
users for the (Position-Based GUAS) is 39.6 seconds. But, from the mean gotten
from table 4.8, the average registration time for (Image-Based GUAS) is 40.8
seconds. Hence, the Position-Based GUAS takes shorter time to register.
System Security
The
primary objective of this research work is to solve the problem of shoulder
surfing attack, hence the development and implementation of the Position-Based
Graphical Authentication System. We evaluated the system to see if it is
resistant to shoulder surfing attack, in comparison with the Image-based
graphical user authentication system. Our Position-Based GUAS is resistant to
both picture capturing and video recording of password (images clicked) during
login. Hence it is resistant to shoulder surfing attack, but the Image-Based
GUAS is not.
System Reliability
After
giving room to 50 participants to test the two systems, they were asked to make
recommendation and individually chose the system that they feel is more
reliable. Their responses are shown below.
Table 9: Responses from Participants based on System Reliability
From the graph above, 66.6% which is equivalent to
33 out of the 50 Participants responded that the Position-based Multi-layer
Graphical user authentication system is more reliable than the Image-Based
Graphical user authentication system.
Individual Preference
Further more, the 50 participants were asked to choose the system that is best for them between the two systems, based on personal preference. The choices they made is shown in the table below.
Table 10: Responses from Participants based on Individual Preference
Participants
Personal Preference
Participant 1
Position-Based GUAS
Participant 2
Position-Based GUAS
Participant 3
Image-Based GUAS
Participant 4
Position-Based GUAS
Participant 5
Position-Based GUAS
Participant 6
Position-Based GUAS
Participant 7
Position-Based GUAS
Participant 8
Position-Based GUAS
Participant 9
Position-Based GUAS
Participant 10
Image-Based GUAS
Participant 11
Image-Based GUAS
Participant 12
Position-Based GUAS
Participant 13
Position-Based GUAS
Participant 14
Position-Based GUAS
Participant 15
Image-Based GUAS
Participant 16
Position-Based GUAS
Participant 17
Position-Based GUAS
Participant 18
Image-Based GUAS
Participant 19
Image-Based GUAS
Participant 20
Position-Based GUAS
Participant 21
Position-Based GUAS
Participant 22
Position-Based GUAS
Participant 23
Position-Based GUAS
Participant 24
Position-Based GUAS
Participant 25
Position-Based GUAS
Participant 26
Image-Based GUAS
Participant 27
Position-Based GUAS
Participant 28
Image-Based GUAS
Participant 29
Image-Based GUAS
Participant 30
Position-Based GUAS
Participant 31
Position-Based GUAS
Participant 32
Image-Based GUAS
Participant 33
Position-Based GUAS
Participant 34
Image-Based GUAS
Participant 35
Position-Based GUAS
Participant 36
Position-Based GUAS
Participant 37
Position-Based GUAS
Participant 38
Image-Based GUAS
Participant 39
Image-Based GUAS
Participant 40
Image-Based GUAS
Participant 41
Image-Based GUAS
Participant 42
Image-Based GUAS
Participant 43
Image-Based GUAS
Participant 44
Image-Based GUAS
Participant 45
Position-Based GUAS
Participant 46
Image-Based GUAS
Participant 47
Image-Based GUAS
Participant 48
Image-Based GUAS
Participant 49
Image-Based GUAS
Participant 50
Image-Based GUAS
Their responses were further represented
using a bar chart, as shown below.
Figure 14: Graphical representation of Performance Evaluation (Individual preference) carried out
From the graph above, 54% which is equivalent to 27
out of the 50 Participants responded that they prefer the Position-based
Multi-layer Graphical user authentication system to the Image-Based Graphical
user authentication system.
Conclusion and Future Work
In
this research work, we developed two systems, Position-Based graphical user
authentication system and an Image-Based graphical user authentication system.
We compared both systems based on three performance metrics (Security,
reliability, Individual Preference).
The
scope of this research work cuts across all sectors. This research will be
beneficial to the society in general, and also help different sectors and
industries to secure their data against intruders.
At the end of this research and after the
comparison, the Position based multi-layer graphical user authentication system
performed better, both in terms of security, reliability and Individual
preference.
References
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Harinandan Tunga, D. S. (2015). Graphical User Authentication Techniques for Security: A Comparative Study . International Journal of Engineering and Advanced Technology (IJEAT), 1-7.
Jiya Gloria Kaka, I. O. (2021). Recognition Based Graphical Password Algorithms: A Survey. 1-10.
Christina Katsini, Christos Fidas, Marios Belk, George Samaras, Nikolaos Avouris. (2019). A Human Cognitive Perspective of Users’ Password Choices in Recognition-based Graphical Authentication. International Journal of Human-Computer Interaction, 1-24. CrossRef
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Amol Bhand, v. d. (2015). Enhancement of Password Authentication system using Graphical Images. International Conference on Information Processing (ICIP), 1-4. CrossRef
Zhili Zhou, C.-N. Y. (2019). Polynomial-Based Google Map Graphical Password System against Shoulder-Surfing Attacks in Cloud Environment. Hindawi, 1-9. CrossRef
Wang, Y. Z. (2020). A Lattice-Based Authentication Scheme for Roaming Service in Ubiquitous Networks with Anonymity. Hindawi Security and Communication Networks, 1-19. CrossRef
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