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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