USER AUTHENTICATION THROUGH MOUSE

DYNAMICS

NURUL LIYANA SYAHIRAH BINTI RUSMADI

BACHELOR OF COMPUTER SCIENCE

(NETWORK SECURITY)

UNIVERSITI SULTAN ZAINAL ABIDIN

2017

USER AUTHENTICATION THROUGH MOUSE DYNAMICS

NURUL LIYANA SYAHIRAH BINTI RUSMADI

Bachelor of Computer Science (Network Security)

Faculty of Informatics and Computing

Universiti Sultan Zainal Abidin, Terengganu, Malaysia

MAY 2017

i

DECLARATION

I hereby declare that this report is based on my original work except for quotations

and citations, which have been duly acknowledged. I also declare that it has not been

previously or concurrently submitted for any other degree at Universiti Sultan Zainal

Abidin or other institutions.

________________________________

Name : Nurul Liyana Syahirah Binti Rusmadi

Date : ..................................................

ii

CONFIRMATION

This is to confirm that:

The research conducted and the writing of this report was under my supervision.

________________________________

Name : Dr. Ahmad Nazari Bin Mohd Rose

Date : ..................................................

iii

DEDICATION

Alhamdulillah, first and foremost, praised to Allah, the Most Gracious and the

Most Merciful for blessing and giving me the opportunity to complete my final year

project, the User Authentication Through Mouse Dynamics.

Here, I would like to take this opportunity to express my highest gratitude to

my supervisor Dr. Ahmad Nazari Mohd Rose and Madam Siti Dhalila Mohd Satar for

their guidance, motivation and help throughout my project. Without their support and

guidance, it is impossible for me to complete my project successfully.

Besides that, I would like to extend my appreciation to my family members

and friends. They have always been there to support and encourage me

unconditionally.

Last but not least, I would like to thank all my lecturers who taught me

throughout my education at Universiti Sultan Zainal Abidin (UniSZA).

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ABSTRACT

Authentication is a process that ensures user identity. Nowadays, the most common

authentication methods used are password and fingerprint-based user. It has been

shown to have some drawbacks as hacker can invade system and revealed the

password while fingerprint can easily be stolen from authenticated user. To overcome

this problem, user authentication through mouse dynamics is introduced. It is an

authentication with pointing device such as mouse and touchpad that can verify

computer user based on their mouse operating styles. The authentication approach is

based on mouse operation task for which user who performs the right action can be

verified as an authenticated user. Other than that the system will reject the user.

Development and testing of biometric system are the main focuses in this study

regarding to the mouse movement for user authentication. Mouse dynamics biometric

system usually consists of three modules which are Data Capture Module, Feature

Extraction Module and Classifier Module. Expected results from this project is that it

can achieve higher percentage in authentication user.

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ABSTRAK

Pengesahan adalah satu proses yang memastikan identiti pengguna. Pada masa kini,

kaedah pengesahan yang paling biasa digunakan adalah kata laluan dan pengguna

berasaskan cap jari. Ia telah terbukti mempunyai beberapa kelemahan seperti hacker

boleh menyerang sistem dan mendedahkan kata laluan manakala cap jari boleh dicuri

daripa pengguna yang sah. Untuk mengatasi masalah ini, pengesahan pengguna

melalui dinamik tetikus diperkenalkan. Ia adalah satu pengesahan dengan menunjuk

peranti seperti tetikus dan pad sentuh yang boleh mngesahkan pengguna komputer

berdasarkan gaya operasi tetikus mereka. Pendekatan pengesahan adalah berdasarkan

tugas operasi tetikus yang mana pengguna yang melakukan operasi tetikus yang betul

boleh disahkan sebagai pengguna yang sah. Selain daripada itu, sistem akan menolak

pengguna. Fokus utama dalam pembangunan dan ujian sistem biometrik adalah

mengenai pergerakan tetikus untuk pengesahan pengguna. Selain itu, sistem biometrik

tingkah laku terdiri daripada tiga modul iaitu ‘Data Capture’ modul, ‘Feature

Extraction’ modul dan ‘Classifier’ modul. Keputusan yang dijangkakan daripa projek

ini adalah bahawa ia boleh mencapai peratusan yang lebih tinggi dalam pengesahan

pengguna.

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CONTENTS

PAGE

DECLARATION i

CONFIRMATION ii

DEDICATION iii

ABSTRACT iv

ABSTRAK v

CONTENTS vi

LIST OF FIGURES viii

LIST OF ABBREVIATIONS ix

CHAPTER I INTRODUCTION

1.1 Background 1

1.2 Problem statement 2

1.3 Objectives 3

1.4 Scope of work 3

1.5 Expected Result 4

CHAPTER II LITERATURE REVIEW

2.1 Introduction 5

2.2 Literature Review 5

2.3 Method used 6

2.3.1 Data Capture Module 6

2.3.2 Feature Extraction Module 8

2.3.3 Classifier Module 9

2.4 Existing Approaches 10

2.4.1 Continuous Authentication Approaches 10

2.3.2 Static Authentication Approaches 11

2.5 Results from Previous Studies 11

2.6 Summary 12

CHAPTER III

METHODOLOGY

3.1 Introduction 13

3.2 Project Methodology 13

vii

3.2.1 Information Gathering Phase 14

3.2.2 Design Phase 14

3.2.2.1 Framework 14

3.2.2.2 Flow chart 16

3.2.2.2.1 Flow chart (Data Capture) 16

3.2.2.2.2 Flow chart (Feature

Extraction)

18

3.2.2.2.3 Flow chart (Classifier) 19

3.2.2.3 Use Case Diagram 20

3.2.2.4 Class Diagram 21

3.2.3 Prototype Phase 22

3.2.4 Review Phase 23

3.2.5 Develop Phase 23

3.2.6 Implement Phase 23

3.2.7 Testing Phase 23

3.2.8 Maintenance Phase 24

3.3 Chapter Summary 24

REFERENCES 25

viii

LIST OF FIGURES

FIGURE TITLE PAGE

3.1 Rapid Prototyping SDLC Model 13

3.2 Framework of the application 15

3.3 Flow chart of Data Capture Module 17

3.4 Flow chart of Feature Extraction Module 18

3.5 Flow chart of Classifier Module 19

3.6 Use Case Diagram 20

3.7 Class Diagram 21

ix

LIST OF ABBREVIATIONS / TERMS / SYMBOLS

GUI Graphical User Interface

FAR False Acceptance Rate

FRR False Rejection Rate

EER Equal Error Rate

USB Universal Serial Bus

SVM Support Vector Machine

KNN Nearest Neighbour

SDLC System Development Life Cycle

MYSQL My Structured Query Language

1

CHAPTER I

INTRODUCTION

1.1 Background

In today’s society, the quest for user authentication and verification become

more important than ever before. Authentication provides a way of clasifying a user

by on their valid username and valid password before they can access the system. In

the context of authentication, biometrics-based authentication is more secure than

traditional based authentication. Moreover, biometrics such as mouse dynamics,

fingerprints, voice, face are less intrusive and do not require any specialized hardware

to capture biometrics information.

The term “biometrics” is borrowed from the Greek words ‘bio’ means life and

‘metric’ is to measure. Biometrics refers to the classification of humans by their

physical characteristics or traits[1]. Biometrics can be divided into two parts that are

physiological and behavioral biometrics. Physiological biometrics is something that

related to part of the body such as fingerprint, voices, face recognition and much more

[1], [2], [4], [5], [8], [10], [11]. On the other hand, behavioral biometrics is related to

the behavior of a person [1], [2], [3], [4], [5], [7], [8], [9], [10], [11]. Mouse dynamics,

signature verification, typing rhythm are some typical examples of behavioral

biometrics. Mouse dynamics is a pointing device such as mouse or touchpad that

2

describes an individual’s behavior. Many previous studies have demonstrated that

mouse dynamics has a rich potential as a biometrics for user authentication with much

lesser cost [2], [3], [4]. An efficient approach that can perform the user authentication

task in short time while maintaining high accuracy is introduced. In this project,

holistic features will be extracted from user’s unique mouse behavior data. Other than

that, Leave One Out Method by using Manhattan Distance are applied.

Mouse dynamics, one of the common behavioral biometrics can be used to

provides user authentication through different mouse movements and clicks. In this

projects, there is a combination of holistic features such as single-click statistic and

movement elapsed will be applied to create user authentication. Mouse biometric

system usually consists of three modules which are Data Capture Module, Feature

Extraction Module and Classifier Module [5]. User will interact with GUI and will

provide raw data based on their mouse behavior for the first module. Second module

work by analyzes the raw data that provided earlier to extract user own feature that

can distinguish each user behavior through their mouse movements. The extraction

feature will then be used to identify or verify user.

1.2 Problem Statement

The most common approach for securing access to system is the use of textual

password. However, it is well known that text password is insecure for a multiple

of reasons. The problem statement of this project, textual password is vulnerable

to attackers as user choose simple and easy to remember password. Textual

password is defenseless against shoulder-surfing, hidden-camera and spyware

attacks. Other than that, users do not pay sufficient attention wisely when choosing

password and also protecting them. Most users think that security

3

1.3 Objectives

The objectives are listed below

i. To identify holistic features of mouse dynamics

ii. To design mouse dynamic application authentication prototype

iii. To test whether the mouse movement can be used to authenticate user.

1.4 Scope of work

There are two scopes in this project which is user and application scope.

User Scope

In the user scope phase, user will interact with Graphical User Interface

(GUI) to register and play with the training programs which is random buttons.

User will be able to provide data regarding user’s unique mouse movements

and clicks.

Application Scope

There are three basic methods that will be used in application scope

which is to capture, extract and classify user’s unique mouse movements and

clicks data that has been provided in the user scope. Application scope can also

able to extract holistic features that include single click statistics, distance,

speed and many more features on how the way user move the mouse based on

the collected data.

4

1.5 Expected Result

Development and testing of biometric system are the main focuses in

this study regarding to the mouse movement for user authentication. This

application is expected to demonstrate that mouse movement behavioral

biometric can be used to authenticate user. The expected results will be

presented in percentage of authenticate user and percentage of non-

authenticate user based on the matching of their mouse movement biometric in

the authentication and verification phase. Expected results from this project is

that it can achieve higher percentage in authentication user which mean that

this application is able to identifies authenticate user by user’s unique mouse

movements and clicks.

5

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

A simple mouse biometric authentication system usually consists of

three modules which are data capture module, feature extraction module and

classifier module [5]. This chapter will discuss about the basic concept of

authentication using mouse movement. Other than that, some of the existing or

related works will be discussed as well.

2.2 Literature Review

“Biometrics” itself derived from Greek words ‘bio’ means life and

‘metric’ is to measure. Biometrics refers to the identification of human by their

traits or characteristics. Biometrics is used as a form of identification.

Biometrics can be categorized into two parts which are physiological and

behavioral biometrics. Physiological biometrics is related to the physical of a

person including iris, fingerprint, face recognition, DNA and many more.

Behavioral are associated to the behavior of a person that includes voices,

mouse dynamics, keystroke and signature [6].

User’s unique profile can be generated by monitoring mouse

movement when user made interaction with GUI. The mouse movement

6

includes mouse move, silence, point and click and drag and drop[7]. Mouse

dynamics are usually evaluated based on the following metrics [3]:

False Acceptance Rate (FAR) – the probability that the system

wrongly denied access to user

False Rejection Rate (FRR) – the probability that the system

wrongly gives authorization to unauthorized user

Equal Error Rate (EER) – the error rate when the system’s

parameter are set such that FRR and FAR are equal. The lower

the EER the more precise the system.

2.3 Method used

Mouse dynamics biometric is designed with three major modules which are

data capture module, feature extraction module and classifier module. Data capture

module consist of an application that can collect data regarding the mouse behavior of

an individual when he or she is interacting with a GUI. The purpose of feature

extraction is to analyze raw data to generate user feature vectors that can be used to

differentiate each user behavior through their mouse movements. Furthermore,

classifier module are used to identify and verify a user based on the extraction feature.

2.3.1 Data capture module

In [4] the raw data was collected from 37 subjects consist of 7

females and 30 males. All subjects were right-handed users and had

been using a mouse for more than two years. All of them were required

to participate in two rounds of data collection per day and waited at

7

least 24 hours between collections. In each round subject need to

perform same mouse operation task 10 times. A mouse movement

sample was obtained when a subject first clicked a start button on the

screen, then moved the mouse to click following buttons prompted by

the data collection application. This experiment took between 15 days

and 60 days to complete data collection. Each subject accomplished

150 error-free repetitions of same mouse-operation task.

In [5] the data capture application was well hidden from the user

and can only function when there is some interaction with the

experiment. User needs to perform an activity called “follow the

button.” The user needs to move the mouse and clicked on the buttons

according to where the buttons appeared. The button was arranged in

random pattern so that user could not predict where would the next

button be. User had to click until the 20th button to finish without any

constraint of time. For this research, user needs to use their own laptop

and mouse within seven days to collect six different data. The raw data

consist of coordinate X and Y and time in milliseconds.

Jorgensen and Ting [3] collected data from 17 volunteer

subjects by using two different types of pointing devices, while

performing a common web browsing task. There are eight males and

nine females that were all computer science student and right handed.

Two identical computers with USB optical mouse and the other with a

USB touchpad were set up. All subjects were given specific web

browsing task designed to last 30 minutes and they need to execute the

task each in both computers with different pointing devices. Jorgensen

8

and Ting create their own custom mouse event logging software which

implement in C# ran as a background process and used Windows

mouse hook to intercept all mouse event then written it to a file.

In [8] mouse movement data was recorded during subjects

routine computing activities. Logging tool RUI were used to record

their mouse movement activities. In order to profile a user’s behavior

on mouse device, cursor movement and mouse event (single, double

clicks and mouse wheel movement) need to be captured [9]. In [7],

they used available mouse dynamics dataset that collected from 49

volunteers. The data collection software stored the dataset in four

features which is type of action (1: Mouse Move; 2: Silence; 3: Point

and Click; or 4: Drag and Drop), travelled distance in pixels, elapsed

time (with 0.25 second accuracy) and direction of movement (a value

between 1 and 8 according the movement of the mouse).

2.3.2 Feature extraction module

According to the research paper [5],the first step was to create

mouse movement profiles. To get mouse movement profile for each

user, the raw data were applied by calculations. The formulas were

time, speed, acceleration, deviation and angle of deviation. Next step

was to create mouse movement profile measurement. Average and

standard deviation were calculated as there would be some counts of

the mouse movement points that could differentiate each user. The

calculation need to be done to find the nearest value from those count

that could distinguish a user to another user.

9

In [8], they first compute the distance, angle and speed between

pairs of data points. For each in every category and for the cursor

movement data, they compute the mean, standard deviation angle, third

moment of the distance and speed between pairs of point. Lastly, they

compute mean, standard deviations and third moment for the X and the

Y coordinates. This will give rough measure where the location of the

events in windows and the location of the cursor in the window.

Besides, research in [4] stated that mouse features were

typically organized into vector to represent the sequence of mouse

operations in one execution of the mouse-operation task. They

characterized mouse behavior based on two basic types of mouse

operations which were mouse click and mouse movement into holistic

features or procedural features. Holistic feature is a feature that

characterizes the whole properties of mouse during interactions such as

single click and double click statistics. On the other hand, procedural

features are a features that describe the detailed dynamic processes of

mouse behaviors, such as movement speed and acceleration curves.

Manhattan Distance are used to calculate the distance vector of holistic

features while, Dynamic Time Warping (DTW) distance are used to

compute the distance vector of procedural features.

2.3.3 Classifier module

In [4], the approach that used to compare and detect the

differences between behavior was using one-class classification. This

approach was an appropriate solution to build a model based only on

10

legitimate user’s data sample. The model used to detect impostors.

Other than that, this research also introduced two other widely used

classifiers which were One Class Support Vector Machine (SVM) and

Nearest Neighbor (KNN) and neural network. Research paper [7] said

that SVM was the only method that always detected the impostors.

Based on a research paper [5],this module was to check the

validity of values from the previous process and classify the patterns

that could identify a user. All values would go through normalization

before the identification process begun. In identification process, Leave

One Out method was used. This method could be done by comparing

or testing a test file value against all the file values in the training data

set by using Euclidean Distance formula.

2.4 Existing approaches

Frequent problems with the existing techniques will be discussed in this

section regarding how they were evaluated. There was two authentication approach

that has been proposed which are continuous authentication approaches and static

authentication approaches.

2.4.1 Continuous authentication approaches

[10] used individual mouse actions (differ in using histogram

over some mouse actions) as a feature for continuous authentication. They

have been used 25 volunteers which are 21 males and four females to collect

data in their experiment and used Random Forest Classifiers for data analysis.

In an approach by Ahmed and Traore [11], low-level mouse events are

11

collection in higher-level actions such as point-and-clicks or drag-and-drops,

represented by action types, distance, duration and direction. Authentication

involves training a neural network on mouse data from a given user which then

be used to classify observed mouse behavior at authentication time.

2.4.2 Static authentication approaches

Static verification approach was when a user required to

perform a series of mouse movement and its mouse data is shown

within a certain amount of time for example in login time. Click-based

graphical password for user login, where five clicks are predicted to be

made in no more than 25 seconds would be a good example of this

scenario [8].

2.5 Results from Previous Studies

Research [4] was focusing on the challenged faced by mouse-dynamics based

user authentication and developed a simple and effective approach that can perform

the user authentication task in short time while maintaining high accuracy. Holistic

features and procedural features are extracted from the fixed mouse operations task to

accurately characterized a user’s unique behavior data. The experiments involved 37

subjects and all of it produced 5550 data samples. The validity of the proposed

approach produced FAR of 8.74% and FRR of 7.69% and an authentication time of

11.8 seconds. The results proved that mouse dynamics could provide a significant

enhancement for traditional authentication systems.

In [5],the results of Leave One Out Method for identification successfully

match 14 matching from 30 data to produce 46.67% of success percentage. The

12

experiment on Randomized Mouse Movement manages to identify 14 identifications

of user. It is quite difficult to get a match for a user or to identify a user in random

environment as the experiment was conducted in an uncontrollable environment

whereby the experiment is tested using user’s own laptop and mouse.

The authors of [8] only focused on fine-grained angle-based metrics which has

two advantages over previous research. The first advantage is angle-based metrics can

distinguish a user accurately with very few clicks. Second, angle-based metrics are

suitable for online re-authentication as it is independent of the operating environment

of user. The experiment gathered two sets of data, one set of 30 under controlled

circumstances, and another set of over 1,000 users on forum websites. They evaluated

the system performance in term of verification accuracy and time. The results were

EER of 1.3% with just 20 clicks. It showed the overhead required for online

verifications is small. Therefore, using partial movements will significantly reduce

verification time, but it will cost of accuracy being degraded.

2.6 Summary

Hopefully, this chapter would provide an overview regarding the concept of

the application. Based on the study that has been made, it shows the literature review

is one of the important parts in research and we could know whether the idea had been

study or not.

13

CHAPTER 3

METHODOLOGY

3.1 Introduction

This chapter discuss about how to develop the project. There are some steps

that must be consider. In methodology term, it is the method that intended to use to

collect data. Methodology is a set of practices regarding to develop the project.

3.2 Project Methodology

Project methodology that used in this project is Rapid Prototyping Software

Development Life Cycle as it can ease of understanding of the system being

developed and missing functionality can be easily detected. The rapid prototyping

SDLC model is shown below in Figure 3.1. This model includes eight main phases

which are information gathering, design, prototype, review, development, implement,

testing and maintenance phase.

Figure 3.1: Rapid Prototyping SDLC Model

14

3.2.1 Information Gathering Phase

Information gathering phase is the most critical step as it determines the

project’s main goal and how the overall system for the mouse dynamics would

function. Analysing user’s requirement is carried out. Basically, all possible

requirement of the application that needed to be developed will be documented

in the requirement specification.

3.2.2 Design Phase

Based on the user requirement and the detailed analysis, the new

system must be designed. The design phase will show how the application

would look like and how its work. In the design phase, the programming

language, the hardware and software platforms in which the new application

will run are also decided. Other than that, desired features and operations in

detailed may include use case diagram, class diagram, framework and other

documentation to know about the flow of the application.

3.2.2.1 Framework

In the mouse movement biometric application, framework and

flow chart was produced before the application was implemented so

that this project would be implemented successfully. Framework

describes about the overview of the system work.

15

Figure 3.2: Framework of the application

Figure 3.2 shows a framework of the authentication through

mouse dyanmics. This framework describe about overview of the

systemwork. The framework can be divided into two phase. There are

registration and verification phase.

For the registration phase, the users will interact with the

program that has been provided by the application. When users start to

play with the application, a monitoring program will be running in the

background of the application and collect raw data. The raw data

consists of co-ordinates X and Y of the mouse movement and time (t).

Next, feature extraction module will extracts holistics features

from the raw data. Holistics features here mean features that

characterized the overall properties of mouse behaviours during

interactions with GUI (citation). Feature vectors is the raw data that

16

has been compute and extracted. Furthermore, the feature vector will

produce training result and it will be stored in the MySQL database.

Moreover, data capture module and feature extraction module

also included in the verification phase. The process are the same as in

the registration phase whereas the feature vector will produce the

practise result. After that, the training result would go through the

classifier module. The results of the practise results in the verification

phase that has been compute would be compared to the training results

in the database from earlier. If the results is the same, the application

wouls authenticate the users.

3.2.2.2 Flow Chart

A flow chart is the type of diagram that represents the process

or the workflow that shows step by step by connecting them with

arrows.

3.2.2.2.1 Flow Chart (Data Capture)

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Figure 3.3: Flow chart of data capture module

Figure 3.3 shows the flow that involves in the data

capture module. Firstly, when the application is start, users will

interact with GUI. The users would input username, ic, gender

and email address to register. Next, user would provide

username and identification number so that the user can play

with the training program. The training program is an

application to collect the raw data from users. The raw data will

then be used to compute features vector in the feature extraction

module.

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3.2.2.2.2 Flow Chart (Feature Extraction)

Figure 3.4: Flow chart of feature extraction module

Figure 3.4 shows the raw data that has been collect from

the user will be stored in the MySQL database. As the results,

the raw data would go through feature extraction to compute

feature vectors.

Moreover, feature vectors that can be extracted from the

raw data are average duration, average distance, average speed,

average single click statistics, variance duration, variance

distance, variance speed, variance single click statistics,

variance double click statistics, standard deviation duration,

standard deviation distance, standard deviation speed, standard

deviation single click statistics and standard deviation double

click statistics. These feature vectors include the training result

and the testing result. The training result is computed during the

19

registration phase while the testing result is computed during the

verification phase.

3.2.2.2.3 Flow Chart ( Classifier)

Figure 3.5: Flow chart of classifier module

Figure 3.5 above shows feature vectors would be stored

in the training program in the database. During the verification

phase, classifier module is responsible to classify feature

vectors. The classifier would compare the testing value and

training value in the database. Users will be authenticate as the

both results match to each other. After the application able to

authenticate user, the program will terminate.

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3.2.2.3 Use Case Diagram

A use case diagram is the most simplest representation of a

system with the user’s interaction. It shows that the relationship

between the user and the different use cases in which the user is

involved. Use case diagrams consist of actors or in other word user, use

cases, boundary and their relationship between actors and the use case.

Figure 3.6: Use Case Diagram

Figure 3.6 shows the first step that the actor which is the user

can involve is registered. Then the next step will be the data capture

process. In this process, raw data from the user are collected. After that,

the process will extend with the feature extraction which will include

feature vector. The last process would extend classifier process. This

process can authenticate user, so the user knows either the application

can recognize the exact user through this classifier process.

21

3.2.2.4 Class Diagram

A class diagram shows the fixed structure of a system. It shows

the objects, attributes, the relationship between classes and all operation

in it. The classes in the class diagram are arranged in groups that share

common characteristics. The classes are represented by boxes that have

three partitioned. The first and the top partition contains the name of

the class. The middle part contains the class’s attributes. The last and

the bottom partition shows the possible operations that are associated

with the class.

Figure 3.7: Class Diagram

22

Figure 3.7 above shows that there are four class for this

application which are user, data capture, feature extraction and

classifier. Each object knows to which class it belongs. The entire users

are able to register the application thus create a relationship called

“register().” The data capture class have attributes called width (x),

height (y) basically the co-ordinates of the random button and time (t).

To extract feature, there will be calculation included such as average,

variance and standard deviation for duration, distance, speed, single

click statistics and double click statistics.

The third class would be feature extraction class. The attributes

for this class is average, variance and standard deviation for duration,

distance, speed, single click statistics and double click statistics. It

involves formulae to calculate distance so it can participate in a

relationship called “CalculateDistance().” Lastly, classifier class which

has distance as attribute. This class can participate in a relationship

called “GetAuthentication().”

3.2.3 Prototype Phase

Prototype is a sample or model of a product built to test a concept. It

shows the main functional capabilities of the proposed application. Prototyping

serves to provide specification for a real, working system, rather than

theoretical one. It includes the sample interface of mouse dynamics

authentication and also the physical database.

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3.2.4 Review Phase.

The sample of the mouse dynamics application prototype that has been

develop will be presented to users. The feedback from user will be collected

for further development.

3.2.5 Develop Phase

The actual projects are developed and built in this phase. Written actual

codes and testing unit is performed in this phase.

3.2.6 Implement Phase

In this phase, each of the design is implemented as one program

module. This project is developed using JAVA programming and use graphical

user interface (GUI) as a medium of interaction and interface whereas MySQL

for physical database. XAMPPServer is used for local host server. Each

module is tested to exclude any kinds of error. Testing the module one by one

is the most efficient ways to debug errors.

3.2.7 Testing Phase

In this phase, data capture module, feature extraction module and

classifier module need to be tested as a whole. This is to make sure that the

application runs smoothly without any errors. If there is any error, the

application would repeat the develop and implement phase until the application

is suitable for users.

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3.2.8 Maintenance Phase

The application must be developed in a way that it would adapt to

change. In this phase, if there is any error detected, all of the problems need to

be solved.

3.3 Chapter Summary

Methodology is very important in system and application development. There

are lots of different software development methodology that available and can be used

to develop any kind of application. All of the activities in each phase in methodology

are explained so that it can be understood easily.

25

REFERENCES

[1] Stephen Mayhew, “History of Biometrics | BiometricUpdate,” January 14,

2015. [Online]. Available: http://www.biometricupdate.com/201501/history-of-

biometrics. [Accessed: 26-Apr-2017].

[2] R. V Ponkshe and P. V. Chole, “Keystroke and Mouse Dynamics : A Review

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