KEYSTROKE DYNAMICS AUTHENTICATION TECHNIQUE

FOR MOBILE ENVIRONMENT

NURAZIZAH BINTI YOUZLAN

BACHELOR OF COMPUTER SCIENCE (NETWORK

SECURITY) WITH HONORS

FACULTY OF INFORMATIC AND COMPUTING,

UNIVERSITY SULTAN ZAINAL ABIDIN

ABSTRACT

Today many people tend to store their sensitive data such as online banking information on

their mobile or smartphone. Therefore, they are highly recommended to strengthen their

existing password. A big issue that could happen is the credentials such as the patterns and

PINs that can be simply hacked by the hackers. Moreover, the normal passwords are

challenging the users to remember because of the combination numbers, letters, and symbols

that can be lost or be stolen. This study would be focusing on the analysis of the biometric

system regarding the typing patterns, formally known as keystroke dynamics as an

authentication technique. This behaviour biometric is focusing on extracting the behaviour

features related to the user and using these features for authentication measures. Besides, this

keystroke dynamics contain three modules that are collection data, feature extraction and

classifier. In conclusion, keystroke dynamics lead to even better authentication performance

than a conventional password.

ABSTRAK

Hari ini ramai orang cenderung menyimpan data sensitif mereka seperti maklumat perbankan

dalam talian di telefon bimbit atau telefon pintar mereka. Oleh itu, mereka sangat disyorkan

untuk menguatkan kata laluan yang sedia ada. Isu besar yang boleh terjadi adalah kelayakan

seperti corak dan PIN yang hanya boleh digodam oleh penggodam. Lebih-lebih lagi, kata laluan

normal mencabar pengguna untuk diingati kerana nombor gabungan, huruf, dan simbol yang

boleh hilang atau dicuri. Kajian ini akan memberi tumpuan kepada analisis sistem biometrik

mengenai pola menaip, secara rasmi dikenali sebagai dinamika keystroke sebagai teknik

pengesahan. Tingkah laku biometrik ini memberi tumpuan kepada mengekstrak ciri tingkah

laku yang berkaitan dengan pengguna dan menggunakan ciri-ciri ini untuk langkah-langkah

pengesahan. Di samping itu, dinamik keystroke ini mengandungi tiga modul iaitu data

pengumpulan, pengekstrakan ciri dan pengelas. Sebagai kesimpulan, dinamik keystroke

membawa kepada prestasi pengesahan yang lebih baik daripada kata laluan konvensional.

Table of Contents

ABSTRACT .............................................................................................................................. 1

ABSTRAK ................................................................................................................................ 3

CHAPTER 1 ............................................................................................................................. 7

INTRODUCTION ................................................................................................................ 7

1.1 Background ..................................................................................................................... 7

1.2 Problem Statement ......................................................................................................... 8

1.3 Objective ......................................................................................................................... 9

1.4 Scope ................................................................................................................................ 9

1.5 Limitation of work ....................................................................................................... 10

1.6 Thesis Organization ..................................................................................................... 10

CHAPTER 2 ........................................................................................................................... 11

LITERATURE REVIEW .................................................................................................. 11

2.1 Introduction .................................................................................................................. 11

2.2 Keystroke Dynamics Authentication ......................................................................... 11

2.3 Keystroke Dynamics Authentication System Design ................................................ 12

2.3.1 Static Authentication ............................................................................................. 12

2.3.2 Continuous Authentication ................................................................................... 13

2.3.3 Data Capture, Feature Extraction, Classifier Modules ..................................... 13

2.4 Classification Technique using Neural Network ...................................................... 16

2.5 Evaluation Performance .............................................................................................. 17

2.6 Summary ....................................................................................................................... 18

CHAPTER 3 ........................................................................................................................... 19

METHODOLOGY ............................................................................................................. 19

3.1 Introduction .................................................................................................................. 19

3.2 Framework ................................................................................................................... 20

3.3 Flowchart ...................................................................................................................... 22

3.3.1 Flowchart (Data Capture)..................................................................................... 23

3.3.2 Flowchart (Feature Extraction Module) ............................................................. 24

3.3.3 Flowchart (Classifier Module) .............................................................................. 25

3.4 Use Case Diagram ........................................................................................................ 26

3.5 Class Diagram ............................................................................................................... 27

3.6 Classifier Algorithms and Measurements .................................................................. 28

3.6.1 Multiplayer Perceptron (MLP) Network ............................................................ 28

3.6.2 Euclidean Metrics .................................................................................................. 29

3.6.3 Manhattan Distance .............................................................................................. 29

3.7 Android Studio ............................................................................................................. 30

3.8 WEKA ........................................................................................................................... 30

3.8 Summary ....................................................................................................................... 31

REFERENCES ....................................................................................................................... 32

CHAPTER 1

INTRODUCTION

1.1 Background

Nowadays, many mobile devices have become a full computing platform. Many people are

using their mobile or smartphone to store data and allow the user to access the internet and

many online services such as to transfer money, manage bank accounts and keep all personal

and public data. These situations are causing an escalation of cybercrime such as the act of a

hacker seeking to steal and manipulate the victim's personal information. Therefore, a user

authorization that contains a high-security mechanism is needed to secure and protect their

assets or personal information from malicious hands. Thus, to improve the security of the

password required, the mobile phone came out with an alternative method, by suggesting the

user use biometrics technology (physical or behaviour) for authentication. This because

biometric-based provides much accurate and reliable security protection because it relies on

unique features for identity verification. Therefore, one of the mechanisms biometric that will

be employed in this researcis keystroke dynamics in the mobile environment. Keystroke

dynamics is a behaviour biometric authentication technology to identify individual unique

characteristic. It is identifying someone based on typing pattern, rhythm, and speed. The

keystroke biometric is more popular because it is cheap than other biometric systems that

require more devices or hardware.

The keystroke biometric system can be categorized into two main processes mainly

authentication and verification. Authentication processes include identifying and verifying

phases that collect data of a user and then allows a user to access the system based on the user’s

identity. This research presents a study on the technique to identify and verify user

characteristics using keystrokes on a mobile environment for user authentication and

verification of a system or an application. Verification is a binary decision problem in which

the system accepts or rejects the identity claimed by the person based on validating a sample

(feature vector) that is collected and compared with the previously collected data for that

person. Meanwhile, the identification is a classification problem where the classifies the input

pattern into one of N knows classes.

1.2 Problem Statement

The authentication process has two primary purposes. Firstly, to identify the correct user who

is authorised to access the resource such as web pages or system and deny the anomaly who

not correctly identified. The secondly is to protect the system from unauthorized use. It is a

critical area of security research and practice. With the increasing demand for more secure

access control in many of security application, keystroke dynamics in the mobile phone is

proposed because of the serval problems from existing traditional access controls.

The first problem involves the identification measures, such as Personal Identification Number

(PIN) or the normal passwords are challenging for the users to remember because of the

combination numbers, letters, and symbols. This makes a user tendency to use simple

passwords and as a consequence, the passwords are easier be stolen by hackers.

The next problem, pattern drawing, and PINs entering are still the most often used by a user

although a mobile has launched fingerprint scanning and biometric data scanning as an

authentication method. The technologies are known to be safer than the former since a simple

pattern and PINs can easily be uncovered leak via surfing attacks. However, the user still

prefers using the pattern drawing and PINs rather than the biometric technologies because

procedures to authenticate a user using biometric technologies sometimes can fail and should

be repeated.

1.3 Objective

The objectives of the research are:

1. To design a keystroke dynamics authentication system in a mobile environment.

2. To implement the keystroke dynamics system for user authentication in the mobile

environment based on data capture, feature extraction, and classifier modules.

3. To test whether the keystroke dynamics can be used to authentication users in the

mobile environment.

1.4 Scope

The main idea of this research is to show the effectiveness of using keystroke dynamics as user

authentication in mobile environments. This research consists of two scopes that are the user

and the system.

Firstly, for the user scope, the user is required to register their username and password in the

registration form that is displayed from the mobile phone. Then, the user needs to enter the

same password (.tie5Roanl) that will be provided from the system 30 times. This stage involves

the process of collecting or capturing data of user typing patterns. The data will convert into

raw data and will be stored in the database system.

For the system, it can capture, extract and classify the keystroke data by using three modules

namely data capture module, feature extraction module, and classifier module. Firstly, the

system will read the user’s keystroke input from the database known as raw data. Secondly,

the raw data would be transformed into the user’s feature and thirdly the data will be classified

using the data mining method.

1.5 Limitation of work

There is the two-authentication scenario, which is static verification and continuous

verification. This research only focusing on static verification. The user required to performing

a system of typing pattern and its feature vector is verified within a certain amount of time, for

example, the login time. Static verification is monitored and verified whenever the user login

to certain applications or services by typing a username and password.

1.6 Thesis Organization

This thesis covers all necessary information about this research. In Chapter 1, this report covers

the introduction of the research where the detail about objectives of the research, the scopes

and the limitation of the research. In Chapter 2, the report mainly covers previous researches

that were used as a reference for this research. Chapter 3 is discussing the methodology of this

research. This chapter explains the framework and flow of the research and all detail about

software and hardware that this research used to produce the results.

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

The main objective of this research is to design a keystroke dynamics authentication system in

the mobile environment based on three modules namely data capture, feature extortion, and

classifier. This chapter will discuss the basic concept of authentication using keystroke

dynamics in the mobile environment. Besides that, a critical and depth evaluation of previous

research will be discussed as well.

2.2 Keystroke Dynamics Authentication

Biometric authentication is the most secure and suitable authentication tools. It cannot be

borrowed, stolen, forgotten and copying. Biometric were based on individual physiological or

behavioural characteristics such as something you know, something you have and something

you are [11]. Physiological biometrics refers to physical measurements of the human body,

such as fingerprints, face recognition, hand or palm geometry, retina or iris. While, behaviours

biometric related to unique behaviours or characteristics of human (user) along time

performing the task include the signature, voice, keystroke dynamics, and mouse movements

[4] [11].

Keystroke dynamics is strong behavioural biometrics that deals with unique characteristics

present in an individual typing rhythm. Keystroke dynamics were recommended as user

authentication first in 1975 and it started from typing rhythms of the user in a computer

keyboard. Then in the year 2002 to 2006, studies about keystroke dynamics on the mobile

environment were reported based on latency between pressing and releasing a key and between

pressing the first key and the last key was used as a feature to user authentication. In the years

2009, Android 1.6 also known as "Donut" was released, more various types of features such as

the size of fingerprint, oriented of devices, and angle of devices were studied. These studies

were increasing from year to year from using various input devices to pressure-sensitive

keyboards to gain data [7].

2.3 Keystroke Dynamics Authentication System Design

Keystroke dynamics behaviour biometric were design with three modules, which are the data

capture module, feature extraction module, and classifier module [8]. The data capture module

consists of an application to collect raw data regarding the typing pattern of the user when the

user entered their information. Then, the feature extraction module extracts a set of features

from the raw data to generate feature vector data. Furthermore, the classifier module is to

authenticate based on their feature vectors.

2.3.1 Static Authentication

The static authentication was referred to as keystroke analysis user characteristic

performance only at a specific time as during the login process [1] [8]. According to

[7], the PIN was introduced by the user serval times during enrolment. The user patterns

time vector was captured and enrolled in keystroke data was gained. Other keystroke

patterns were extracted, and their mean, standard deviation and median were calculated

which was given as the input to the feature subset selection.

2.3.2 Continuous Authentication

The continuous authentication analysis were referred to the user-typing pattern that

monitored for the entire duration for which the user logged in [8]. This method provided

a tool to detect user substitution after a successful login. The free text model was a

continuous authentication system that looking or continuously detected the presence of

an authorized user. The benefit in this situation, where the system was taken over by

the hacker, the system would be automated to detect the hacker as an unauthorized user

[7].

2.3.3 Data Capture, Feature Extraction, Classifier Modules

The studies of keystroke dynamics consisted of three modules: data capture, feature

extraction, and classifier modules. In data capture, the raw keystroke data will collect

through typing rhythm such as time-base measures. The next module is to extract the

different features from the raw data and transform them into a processed classification.

In this literature review, a few kinds of research focus on touch keystroke features. In

2015, Antal and Szabo [10] studied how the finger area and finger pressure as the

feature would affect the identification and verification performance in mobile devices.

They asked 42 participates to type the same password (.tie5Roanl) 30 times in 2

sessions and the data were collected software was implementation on Nexus device

running the Android operation system. Four features were extracted consisting of the

dwell time, flight time, and finger area and finger pressure. For identification, they use

Naïve Bayes, Bayesian Network, J48, k-NN, SVM, Random Forest, and MLP

classifiers. The random forest showed 82.5% and 93% of accuracy by using the data.

Nang Zeng et.al. [11] proposed a non-intrusive user verification mechanism using a 12-

key touchable keyboard. The five features set was collected from 80 users through an

Android application such as acceleration, pressure, touch size, key-hold and inter-key

time. They used the nearest network algorithms to classifier the data. The ERR for 4-

digit password data was showed 3.65% error while 8-digit password data show 4.55%

error. They also tested on how the feature contributed to the final accuracy, and they

found that the combination of all feature sets always outperformed the individual

feature set.

On the other hand, several studies showed that keystroke dynamics and typing patterns

behaviour could detect the characteristics of the user for authentication. Based on the

statistical keystroke dynamics system measure, Mahmood and Al-Jarrah [12] proposed

an evaluation of the authentication performance of the implemented distance-to-median

anomaly detector. The system worked in the android environment on the Nexus

smartphone or tablet. The system generated a dataset of 2856 that was recorded from

56 subjects where each record represented 71 feature elements from the typing of

standard 10-password character. The result of the anomaly detector model showed that

4.9% compare to other 71 features detector in [10].

Since the year of 2010, android 2.3 provided data from gyroscope, rotation vector,

linear accelerometer, and gravity. Thus, more features could be extracted from them.

[7] conducted the experiment on how the feature from motion data could be effective

in keystroke dynamics authentication. The five features set were collected from 22 users

with the addition of motion data through the android application Nexus 5x. The data

sample of the user typing the 6-digit numeric PIN was classifier using the simplest

algorithm, distance-based algorithm. The result showed that the improvement with

motion 7.8% than without motion 8.9%.

2.4 Classification Technique using Neural Network

Figure 2.1: Architecture of Neural Network

An artificial neural network is a class of machine learning methods that were based on

mathematical models of neurons (also called nodes) organized into layers to model complex

relationships between the input and the output. The basic neuron consisted of an input, a

weight, a bias and output [1] [17]. The neural network was used for keystroke dynamics was

BPNN, RBFN, PNNN, and FF-MLP.

2.5 Evaluation Performance

The main concept of keystroke dynamics as an authenticator that can detect the unique patterns

that exited when the user interacts with the keyboard on the screen. These patterns can be

organized in many different ways including statistical classifiers or using neural networks.

There two types of errors that used to measure the result of classification, false acceptance error

(FAR) and false rejection error (FRR) [7] [9] [15]. FAR means they indicate an error of

accepting an imposter user as a legitimated user. It was also known as the false-positive rate.

FAR told the system whether it is a secure authentication system or not. The higher the FAR

the easily the attacker goes through the system. On the other hand, FRR means the percentage

of legitimate users considered intrudes and rejected by the system [8] [9]. FRR can be told the

completeness of the system whether the system is usable or not. If FRR is high, the user has to

retry the authentication repeatedly until user success entered to the system.

EER stands for Equal error rate that exists when FAR and FRR was equal. Generally, reducing

FAR increase FRR. EER is the index used for performance evaluation of behaviour-based

authentication [15].

Figure 2.2: the relationship between the FAR, FRR and EER

2.6 Summary

Based on the literature review on existing paper and journal, there are many ways to make

keystroke dynamics effective to be used as user authentication. In my research, I will use statics

authentication models in user authentication where users need to type strings during a login

process. For data capture, the data gain when user typing and the data will store into the

database. Then the data will extract to feature vector in feature extraction. This process will

analyse for user authentication. Lastly, I will use neural network algorithms in the classifier

module will measure the accuracy of the authentication user based on their keystroke typing

patterns.

CHAPTER 3

METHODOLOGY

3.1 Introduction

The methodology is a particular procedure or set of procedures. A suitable methodology plays

an important role to ensure the research can be done. This chapter will focus on the

methodology used in this research and this chapter will explain in detail every method that will

be used and implemented in this research.

3.2 Framework

Figure 3.1: Framework

Figure 3.1 above shows a framework of the keystroke dynamics authentication on the mobile

environment using a biometric system. This framework describes an overview of the system.

The keystroke dynamics in the mobile environment biometric system consist of three modules:

data capture, feature extraction module, and classifier module. There are two modes in

keystroke dynamics in the mobile biometric system: enrolment and verification. This system

also consists of two programs, training program, and testing program.

Firstly, when a new user enrols in the system, the system will ask the user to input their

username and email. Then the system asks the user to input the same password for 30 times.

When users start to enter the first character of the password, a data capture module will start

running in the background of the system. The data capture will collect data and stored it into

raw data. The raw data will consist of the time of key holders and other features.

Secondly, the feature extraction module extracts a set of features from the raw data. The raw

data that have been extracted and compute would be the feature vectors. The feature vector will

produce the training result and stored it into the database system.

Besides, the verification phase is also involved in the data capture module and a feature

extraction module. After the feature vector produces the testing result, this testing result would

go through the classifier module. This classifier module will use the neural network method as

an algorithm and measure the accuracy of the system. This classifier module also will compare

the training result in the database system with the testing result. If the result is the same, the

system will authenticate the user.

3.3 Flowchart

A flowchart is a type of diagram that represents an algorithm, workflow or process. Flowcharts

are used in analysing, designing, documenting or managing a process or program in various

fields. The flowchart of the main steps in the keystroke dynamic for the mobile system can be

visualized as in Figure 3.2.

Figure 3.2: Flowchart

3.3.1 Flowchart (Data Capture)

Figure 3.3: Flowchart of the data capture module

Figure 3.3 shows the flow that involves the data capture module. Firstly, when the

system is starting, the user needs to input user details for the user registration process.

If the user registers success, Keystroke Dynamics authentication for mobile system will

proceed with the training program where the user needs to enter their username and

enter the same password (.tie5Roanl) for 30 times. This process will collect user typing

patterns details. Then this collected data will be saved into raw data files. This raw data

is very important to compute the feature vector in the feature extraction module.

3.3.2 Flowchart (Feature Extraction Module)

Figure 3.4: Flowchart of the feature extraction module

Figure 3.4 shows the raw data that have been collected from the user would be stored

in the database. Then, the raw data would go through the feature extraction module. To

obtain the feature vector, feature measurements are computed from the raw data file.

Feature vectors include the time of key hold, flight time, dwell time, finger area, key

hold pressure, acceleration, average and standard deviation for all features. The feature

vectors are stored in the training program table in the database. The feature vectors are

used to come out with training results and testing results in the classifier module. The

training result is computed during the enrolment phase while the testing result will

compute to complete the verification phase.

3.3.3 Flowchart (Classifier Module)

Figure 3.5: flowchart of classifier module

Figure 3.5 above shows the feature vectors that are extracted would be stored in training

tables in the database. During the verification phase, the classifier module is using a

neural network method as an algorithm and to measure the accuracy of the system. This

classifier module will compare the exact value of the testing result with training results

in a database. If both results are the same, the keystroke dynamics system would

authenticate the user. If not, the system will automatically terminate users from the

system.

3.4 Use Case Diagram

A use case diagram is a graphic representation of the interaction among the elements of an

application that is used in system analysis to identify, clarify, and organize application

requirements.

Figure 3.6: Use Case Diagram

Figure 3.6 shows that the actor, which is used, can register the system to verify user identity

before continues to another stage. User needs to register their information such as username

and password. The system will capture user-typing pattern and the information will be saved

as raw data. This raw data will be saved into the database in the training program. This process

is to ensure that only the user with the same credential in the database system can access the

system and can consider as an authentication user. After that, the process will proceed or extend

with the feature extraction process. In this process, the feature vector will include the time of

key hold, flight time, dwell time, finger area, key hold pressure, acceleration, average and

standard deviation for all features. Lastly, the third process would extend to the classifier

process. In a classifier process, the data-mining algorithm will be used to compare the feature

vector of the testing result with the training sample. If the testing result is the same with the

training sample set, the keystroke dynamics system will recognize it as a legitimate user and if

not, the user cannot access the system.

3.5 Class Diagram

Figure 3.7 shows the class diagram. A class diagram is an illustration of the relationship and

source code dependencies among classes in Unified Modelling Language (UML). The class

diagram is useful in all forms of object-oriented programming (OOP). There is four class such

as user, data capture, feature extraction, and classification. In a class diagram, the classes are

arranged in groups that share a common characteristic.

Figure 3.7: Class Diagram

3.6 Classifier Algorithms and Measurements

This chapter will discuss the algorithms that will be used in this research. As a behaviour

biometrics authentication, keystroke dynamics authentication makes use of unique rhythms and

behaviour when typing a key or character on keyboards. The algorithms will decide the result

of the user being authenticate or not. In this research, a neural network classifier would be

implemented to classify the users and this neural network is based on a feature vector to

measure the accuracy. Moreover, the Euclidean distance and Manhattan distance are used to

define distance metrics.

3.6.1 Multiplayer Perceptron (MLP) Network

A multilayer perceptron (MLP) is a feed-forward artificial neural network model that

maps sets of input data onto a set of appropriate outputs. MLP consists of multiple

layers of nodes in a directed graph, with each layer fully connected to the next one.

MLP uses a supervised learning technique called backpropagation for training the

network. MLP is a modification of the standard linear perceptron and can distinguish

data that are not linearly separated [10]. It consists of three main parts: an input layer,

one or more hidden layers, and an output layer. The input layer distributes the input

data to the processing elements in the next layer. Next, the hidden layer combines the

linear and the nonlinearity behaviour and the last stage shows the output layer. Input

and output are directly accessible, while the hidden layers are not. Each layer consists

of several neurons.

Figure 3.8: MLP Neural Network

3.6.2 Euclidean Metrics

The Euclidean distance is calculate the distance between two n-dimension

vectors,𝑝(𝑝1, 𝑝2, … , 𝑝𝑛), and 𝑞(𝑞1, 𝑞2, … , 𝑞𝑛) as a straight line and the formula is given

by

d (p,q) =√(𝑞1 − 𝑝1)2 + (𝑞2 − 𝑝2) + ⋯ + (𝑞𝑛 − 𝑝𝑛)

=√∑ (𝑞𝑖−𝑝𝑖)𝑛𝑖=1 [7]

3.6.3 Manhattan Distance

The Manhattan distance calculate the distance between two n-dimension vectors,

𝑝(𝑝1, 𝑝2, … , 𝑝𝑛), and𝑞(𝑞1, 𝑞2, … , 𝑞𝑛), by subtracting the value and then summing the

absolute of them as follows:

𝑑(𝑝, 𝑞) = |𝑞1 − 𝑝1| + |𝑞2 − 𝑝2| + ⋯ + |𝑞𝑛 − 𝑝𝑛|

= ∑ |𝑞𝑖 − 𝑝𝑖|𝑛𝑖=1 [7]

3.7 Android Studio

Android studio [2] is the official Integrated Development Environment (IDE) for Android app

development, based on IntelliJ IDEA. On top of IntelliJ powerful code editor and develop tools,

Android Studio offers even more features that enhance productivity when building Android

apps and system such as:

Flexible Gradle-based build system

Fast and feature-rich emulator

The unified environment where can develop for all Android device

C++ and NDK support

3.8 WEKA

WEKA is an open-source software provides tools for data pre-processing, implementation of

several Machine Learning algorithms, and visualization tools so that can develop machine

learning techniques and apply it to real-world data mining problems [10] [16] .

3.8 Summary

This chapter discussed the methodology approach to be used in the development of the

application. The Keystroke Dynamics Authentication Technique for Mobile Environment

system used three modules, which are the data capture module, feature extraction module, and

classifier module. Every methodology that would be used was illustration using an image such

as framework, use case diagram, flowchart, and class diagram. This research uses the neural

network method to classifier the trusted user and illegitimate user. The activity in each phase

in the methodology is explained so that it can understand easily.

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