“User Identity verification via mouse dynamics”

Under the Guidance of –

Prof. D.V. Kodavade

Head & Associate Professor, Department of CSE,

D.K.T.E Ichalkaranji, Kolhapur.

Sumitted By –

Mr. Gorad Balwant Jaywant

M.Tech –II(CST),

Department of Technology, Shivaji University, Kolhapur.

Index Introduction

Choice of the topic

Literature Review.

System Architecture

System Requirement and Design

Implementation

Experiments and Results

Conclusion and Future Enhancements

Bibliography

List of Journals and Publications

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Obviously, Everyone knows about the Hacking, and it is a crime,

Because no one wants to share all his private data with public.

And todays systems are not guarrenting the full security, Hackers

can easily steal the credentials of computer by using various

techniques such as phishing attack, key loggers and many more

different attacks.

This method gives one more security layer with addition to the

existing credentials of the system, so it provides better security to

the computers.

1. Introduction-

3

.

The drawback of normal identification methods that are

based only on credentials, leads to the introduction of user

authentication and verification techniques, that are based on

behavioral and physiological biometrics which are assumed to

be unique to each other and hard to steal.

So for good security we should perform authentication as well

as verification.

In this system, authentication is performed once during the

login to the computer while verification is performed

continuously throughout the session by drawing his/ her

private mouse dynamics.

Following table shows some of biometric techniques and their

accuracies.

4

Biometric

TechnologyAccuracy Cost

Device

RequiredAcceptability

Iris Recognition High High Camera Medium-low

Retinal Scan High High Camera Low

Face RecognitionMedium-

lowMedium Camera High

Voice Recognition Medium Medium Microphone High

Finger Print High Medium Scanner Medium

Signature

RecognitionLow Medium

Mouse, Optic

Pen, Touch

Panel.

High

Hand Geometry Medium-low Low Scanner High

Table No. 1 Overview of Biometric Technologies5

.

Currently most of the computer Systems and online websites

identifies the users by means of usernames and passwords/

PINS. But normally hackers can easily steal the password.

There are so many techniques which are used to hack the

username and passwords of the systems. Some of the techniques

are phishing, key loggers and many more.

So there is need to improve security level of existing computers.

This proposed approach gives one more additional security layer

to the existing security layer which uses mouse dynamics

verification.

2. Choice of the topic-

6

.

User verification can be of two types – either it physiological or

behavioral.

The drawback of physiological verification methods is that they

require dedicated hardware devices such as fingerprint sensors

and retina scanners which are expensive and are not always

available.

But Behavioral biometrics, on the other hand, do not require

special designated hardware since they use common devices such

as the mouse and keyboard.

Mouse verification can be used effectively than keyboard

dynamics, so user identity verification using mouse dynamics is

selected for proposed work.

7

.

Most common behavioral biometrics verification techniques are

based on:

(a) mouse dynamics [1] [2] [8], which are derived from the

user-mouse interaction and the focus of this implementation is

based on mouse dynamics of the user;

(b) keystroke dynamics [7] [10], which derive from the

keyboard activity; such frequency of key pressing, typing

speed, etc and

(c) software interaction, which rely on features extracted

from the interaction of a user with a specific software tool.

3. Literature Review-

8

.

3.1 Mouse Based Approaches

This type of Authentication methods, identifies users at login

based on a predetermined sequence of mouse operations that

the user needs to follow.

During training, the features of mouse operation for the

particular user is stored. These features are used to

characterize the user during the verification. During

verification, the user is required to follow the same sequence.

Two types of mouse based approaches we have-

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.

3.1.1. Explicit learning methods- Author Hashia [13]

used a sequence composed of pairs of points. Each user

was required to move the mouse between the first and

second point in each pair where features were extracted

from each movement.

The method proposed by Gamboa [12] required the users

to enter a username and a pin number using only the

mouse via an on-screen virtual keyboard. Authentication

combined the credentials and the mouse dynamics of

their entry.

3.1.2. Implicit learning methods- Pusara and Bordley [15]

explained a method to detect anomalous behavior

using the current user's mouse movements.

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.

3.2 Keyboard and Software Approaches-

Alternative approaches to user verification utilize keyboard

dynamics and software interaction characteristics.

Ling, Luiz[7] and Chan and Han[10] implemented methods based

on keyboard dynamics, for example, features considered are

latency between consecutive keystrokes, typing speed, flight

time, dwell time - all based on the key down/press/up events.

Keyboard-based methods are divided into methods that analyze

the user behavior during an initial login attempt and methods that

continuously verify the user throughout the session.

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Before the discussion of proposed system and its architecture, let

us discuss something about general behavioral biometrics system.

A biometric- system is essentially a pattern recognition system

that acquires biometric data from an individual, extracts a feature

set to establish a unique user signature and constructs a

verification model which classifies authenticated user and non

authenticated user.

Fig.1 shows the general behavioral biometric system

4. System Architecture-

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Such systems include the following components:

Feature acquisition – captures the events generated by the

various input devices used for the interaction (e.g. Keyboard,

mouse) via their drivers.

Feature extraction – constructs a signature which characterizes

the behavioral biometrics of the user.

Similarity Match / Decision Taker – This is used to build the

user verification model, which will take a decision about either

computer system will shut down or it will continue the work.

During verification, this model is used to classify new samples

acquired from the user.

Signature database – A database of behavioral signatures that

were used to train the model. Upon entry of a username, the

signature of the user is retrieved for the verification process.

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4.1 The Proposed System Architecture

Figure.2 Architecture of Proposed System14

The system is classified mainly into four components, which are

as follows.

4.1.1 Feature acquisition – System captures the events

generated by the various input devices used for the interaction

(e.g. Keyboard, mouse) via their drivers. This Approach totally

prefers mouse interaction with computer systems as shown in

fig. 2.

(i) Mouse-move Event(m) (ii) Left Button down(ld)

(iii) Right Button down Event(rd) (iv) Left Button up(lu)

(v) Right Button up Event(ru) (vi) Silence(s)

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.

4.1.2. Feature Extraction – constructs a signature which

characterizes the behavioral biometrics of the user. Please

refer fig. 2 to get overall idea of feature extraction from users

mouse dynamics.

Higher level features incorporate dependencies between

lower-level ones which help to characterize more accurately

every user.

For Example, a mouse left click contains two low level

events such as left down and left up.

Second example we would like to give that, MMS (Mouse

Move Sequence) is composed of multiple mouse move

events in between silence interval is present.

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.

In the proposed hierarchy, Following are the features are

considered for extraction.

Left Clicks (LC)

Right Clicks (RC)

Double Clicks (DC)

Mouse Move (MM)

Area under Curve (AUC)

Eccentricity (ECC)

Total Time (TT)

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I. Left Clicks (LC) – refers to the action of clicking on the left

mouse button. This action consists of a left button down event

followed by a left button up event taking place within specified

τLC seconds from the button down event.

Formally,

Where ld = left down, lu = left up, m1, m2 ...mn = mouse

move events and τLC = specified time interval

Fig. 3 Left Click feature

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II. Right Clicks (RC) – refers to the action of clicking on the

right mouse button. This action consists of a right button down

event followed by a right button up event taking place within

specified τRC seconds from the button down event.

Formally,

Where rd = right down, ru = right up, m1, m2 ... mn = mouse

move events and τRC = specified time interval

Fig. 4 Right Click feature

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III. Double Clicks (DC) - is composed of a two consecutive

left clicks or right clicks in which the mouse-up of the first click

and the mouse-down of the second one occur within an

interval of τI seconds.

Formally:

Fig. 5 Left Click feature

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IV. Mouse Move (MM) - A sequence of mouse-move events

followed by silence time σ.

Formally,

MM = MMS.σ

Fig. 6 Left Click feature

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V. Area Under Curve (AUC) – Actual number of pixels in the

region.

The initial value of pixel is 0; That is currently Area = 0;

Formally,

Current Area = Current Area + 100/(Image Height * Image Width)

Pixels = Image.getPixel(x1,y1)

Where x1 < Width of image and y1 < Height of Image

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VI. Eccentricity (ECC) –

The ratio of the distance between the foci of the ellipse

and its major axis length.

Eccentricity of an ellipse is a measure of how nearly

circular the ellipse. It is found by following formula,

Eccentricity (ECC) = C/A

Where C is the distance from the center to focus of the

ellipse and A is the distance from center to vertex.

Fig. 7

Eccentricity

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VII. Total Time (TT) – This feature calculates the approximate

time required to draw a mouse dynamics to the trusted third

user. Standard timer is used in the C# language to calculate the

time required to draw a mouse signature. For Example timer

starts when the respective form loads and it stops when we

press the Extract button which is present on standard GUI.

So total time required to draw a signature can be

Calculated is as follows

Total Time (TT) = T2-T1

Where

T2 = Time When we finish the Signature And

T1=Time when we start the drawing

Signature (When form loads).

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4.1.3. Similarity Match / Decision Taker –

This is used to build the user verification model by using a

considerable threshold. During verification, this model is used to

classify new samples acquired from the user.

As we know we can‟t draw the same signature every time with a

pen also, so it‟s very difficult to draw the same signature by mouse

into the canvas, So threshold plays an important role in this approach.

A Classifier /similarity match takes the decision either system

has to continue the login or logout based on a similarity match

between the user dynamics drawn during the registration and during

the verification.

This component takes the value of the percentage of Matching

(POM) from the previous step and decides either computer will shut

down or it will continue the login as shown in fig. 2

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So, Percentage of Match (POM) is calculated with the help

of following formula.

Final Percentage of Matching (POM) =

POM (in LC) + POM (in RC) + POM (in DC) + POM

(in MM) + POM (in AUC) + POM (in ECC) + POM (in TT) .

Another Factor used in Classifier is PVM (Predefined Value Set for

Matching), This can be decided by administrator of the system

PVM is the criteria to set the security level.

IF POM≥ PVM… User Access to computer is Granted

Else User Access is Denied

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This whole process should perform multiple times, so that trusted

third parties will get more chances to prove his/her authentication

and illegal users will have more difficulties to prove he/ she is an

authorized multiple times.

The final decision taken by the decision taker (either it is

authenticated or not authenticated) will get the decision on his/her

registered mobile. Also what action took by the system it will also

be conveyed, Action may be computer system remains login or it is

going to shut down.

Following two Conditions may be there.

Condition Message on Mobile Action Taken by Decision Taker

Table2. Decision Table27

Software Requirements

Operating System:

Windows 2000/XP/2003/Vista/7/8

Microsoft Visual Studio 2008, 2010

(MS VS2010 Recommended)

Microsoft SQL Server 2005/ 2008

(2008 Recommended)

Microsoft Visio 2010 suite.

Hardware Requirements

Minimum Requirements:

Intel Pentium 4 & above

1 GHz processor

512 MB RAM

Recommended system:

Intel Core i3 or Above processor,

4 GB RAM or Above

Hard Disk Drive 320 GB

Optical Mouse (Recommended)

Intel processor is recommended for better performance.

5. SYSTEM REQUIREMENTS AND DESIGN

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5.1 Design using Dataflow diagrams

Fig. 8 Data flow diagram for

registration

Fig. 9 Data flow diagram for

verification29

5.2 Activity Diagram

Fig. 10 Activity diagram of registration process

Fig. 11 Activity diagram of verification process30

5.3 Project Flow Diagram

Fig. 12 Project flow diagram 31

6. IMPLEMENTATION

The implementation of the proposed system is carried out using

C# programming language and by using Microsoft Visual Studio 2010

editor.

6.1. Implementation of Mouse Database

The database usually contains unlimited tables and in one table

usually can store unlimited users. Along with users their mouse

signature features are also maintained.

During the verification phase the stored features of particular

users can be retrieved for verification with the help of the username.

So it‟s mandatory to give unique username during the registration

phase.

The database can be created with Microsoft SQL Server 2008

which is inbuilt in visual studio 2010.

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Table NoName of

databaseTable names Name of the columns Purpose of creation

1 MouseDB AddUserTable

Id, username, password,

Mobile, Signature,

question, ans

To Add New User into

System.

2 MouseDB feature

User_name, Area,

Double_clk, Eccentricity,

mouse_mvc,

Total_time,Left_clk,

Right_clk

To Store the features

those are extracted from

user drawn mouse

dynamics / mouse

signature.

3 MouseDB chk chk, matchTo check valid matching

or invalid matching.

4 MouseDB count1 count, validity

To check number times

valid verification and the

number of times invalid

verification.

5 MouseDB MainLogin UserNm,UserPassTo access the system, its

main login to system.

6 MouseDB temp username, pathTo store the mouse

signature image path.

Table3. Database tables

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6.2 Graphical User Interface Implementation

The project entitled “User Identity Verification via mouse

Dynamics” is divided into several modules as we consider for

implementation such as Registration of user, Drawing Signature,

Extracting the Features and Storing signature in Mouse Database,

User Verification, Decision Taking, etc.

Following video will shows us the GUI of this system and how

this system will work.

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7. EXPERIMENTS AND RESULTS

Experiment 1-

This first experiment is conducted to test the authentication

and non authentication for the respective users.

Obviously if the user is able to draw the same dynamics then

and then only user will be authenticated else it is not

authenticated.

Same Username, Password, Mobile Number and Favorite

Number are used to conduct the experiment.

Fig. 13 Sign during registration Fig. 14 Sign during verification

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Sr.

NoFeatures Extracted

Value During

Registration

Value During

VerificationFinal Decision

1. Left Clicks (LC) 14 03

16% Match

Not

Authenticated

User

2. Right Clicks (RC) 6 0

3. Double Clicks (DC) 4 0

4.Mouse Move (MM)

pixels1147 1013

5.Area Under Curve

(AUC) pixels31562 22801

6. Eccentricity (ECC) 0.4778 -0.0957

7.Total Time (TT)

Seconds19 17

Table.4 Result of experiment 1

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Experiment 2-

This second experiment is conducted to test the authentication

and non authentication for the respective users if they are drawing

same signature.

Obviously if the user is able to draw the same dynamics then

and then only user will be authenticated else it is not

authenticated. Also same username, password, mobile number

and favorite number are used to conduct the experiment.

Fig. 15 Sign during registration Fig. 16 sign during verification38

Table.5 Result of experiment 2

Sr. No Features ExtractedValue During

Registration

Value During

VerificationFinal Decision

1. Left Clicks (LC) 14 14

88% Matched

Authenticated

user

2. Right Clicks (RC) 6 6

3. Double Clicks (DC) 4 4

4.Mouse Move (MM)

pixels1147 1085

5.Area Under Curve

(AUC) pixels31562 31320

6. Eccentricity (ECC) 0.4778 0.5172

7.Total Time (TT)

Seconds19 18

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Experiment 3

This is a general experiment, in this experiment different

possibilities of signature drawing are considered. A set of

signatures has been taken to test it with stored signature in a

database. In such cases the matching gives a similarity value

depends on how the signature is drawn by the user.

Fig.17 Registered user signature for experiment 3

40

Sr.

NoUser Signatures

% of

Match

Sr.

NoUser Signatures

% of

Match

1 28% 4 88%

2 88% 5 43%

3 85% 6 82%

Fig.18 General Experiment with results41

8. CONCLUSION AND FUTURE ENHANCEMENTS

8.1 Conclusion

A novel method for user verification based on mouse activity

is implemented in this work. Common mouse events performed in

a GUI environment by the user is collected and a hierarchy of

mouse actions is defined based on the raw events.

In order to characterize each action, features are extracted.

A two-layer verification system is implemented. The system

employs a feature extraction in its first layer and a decision

module in the second one in order to verify the identity of a user.

The implemented method is evaluated using a dataset that is

collected from a variety of users and hardware configurations.

42

As per experiments conducted, better accuracy is achieved than

histogram technique. The observation in experiment 3.2, 3.3, 3.4

and 3.6, shows that better accuracy is observed when the

respective user is trying to behave as the same what he behaved

during the registration.

In experiment 3.2, the achieved accuracy is 88%, Experiment

3.3 it is 85%, Experiment 3.4 it is 88% and Experiment 3.6 it is

82%. As per experiments conducted experiment 1, 3.1 and 3.5,

accuracy is collapsing if user tries to misbehave, which is shown in

results of experiment 1, the achieved accuracy is 16%,

Experiment 3.1 accuracy is 28% and Experiment 3.5 accuracy is

43%, which is less than the predefined threshold, hence it is a sign

to the computer system that it will no longer continue.

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8.2 Future Enhancements

In the following we describe several issues that need further

investigation in mouse-based verification methods.

The original actions intended by the user are logged neither by

software nor by observing the user while performing the actions.

Accordingly, they are heuristically reconstructed from the raw events

which may produce some non-credible actions.

Additionally, the obtained actions may vary between different

hardware configurations (e.g. Optical mouse, touch pad). In order to

obtain a higher percentage of credible actions, the parameters that

define them should be determined by a more rigorous method.

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8.3 Applications

Due to the advances in technology, it is quite easy to crack the

security systems available today. Biometrics is the only mechanism

which is comparatively more secure than other traditional methods.

Also it provides one more additional security layer to the

existing security layer. This system aimed at improving the security of

the biometric system that uses mouse dynamics/ mouse signature

features. The applications of this system are not limited to a specific

area.

Some of the applications are as follows.

Banking sector, Any kind of electronic devices- From desktop

computers to PDAs, Mobile to palmtops, Research laboratories,

Electronic voting machines, ATM counters, Emails and many more..

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9. BIBLIOGRAPHY

9.1 Document References

1. Clint Feher, Yuval Elovici, Robert Moskovitch, Lior Rokach, Alon Schclar, “User

identity verification via mouse dynamics”, Information Sciences 201 (2012)

19–36.

2. Chao Shen, Zhongmin Cai, Xiaohong Guan, Youtian Du, and Roy A. Maxion,

User Authentication Through Mouse Dynamics. IEEE TRANSACTIONS ON

INFORMATION FORENSICS AND SECURITYVOL. 8, NO. 1, Jan 2013

3. Zach Jorgensen and Ting Yu, On Mouse Dynamics as a Behavioral Biometric for

Authentication. ACM 978-1-4503-0564-8/March 2011.

4. Z. Jorgensen, T. Yu, “On mouse dynamics as a behavioral biometric for

authentication, in: Proceedings of the Sixth ACM Symposium on Information,

Computer, and Communications Security” (AsiaCCS), March 2011

5. M. De Marsico, M. Nappi, D. Riccio, G. Tortora, NABS: “novel approaches for

biometric systems, IEEE Transactions on Systems, Man, and Cybernetics”, Part

C: Applications and Reviews 41 (4) (2011) 481–493.

46

6. Saurabh Singh, Dr K V Arya, “Mouse Interaction based Authentication System by Classifying the Distance

Traveled by the Mouse” International Journal of Computer Applications (0975 – 8887) Volume 17– No.1,

March 2011

7. Lívia C. F. Araújo, Luiz H. R. Sucupira Jr., Miguel G. Lizárraga, Lee L. Ling, andJoão B. T. Yabu-Uti, “User

Authentication through Typing Biometrics Features, IEEE Transactions on Signal Processing”, Vol. 53, No.

2, February 2005

8. P. Bours, C.J. Fullu, “A login system using mouse dynamics, in: Fifth International Conference on Intelligent

Information Hiding and Multimedia Signal Processing”, 2009, pp. 1072–1077.

9. S. Bleha, C. Slivinsky, B. Hussein, “Computer-access security systems using keystroke dynamics, IEEE

Transactions on Pattern Analysis and Machine Intelligence” 12 (12) (1999) 1217–1222.

10. S. Cho, C. Han, D.H. Han, H.I. Kim, “Web-based keystroke dynamics identity verification using neural

network, Journal of Organizational Computing and Electronic Commerce” 10 (4) (2000) 295–307.

11. L. Ballard, D. Lopresti, F. Monrose, “Evaluating the security of handwriting biometrics, in: The 10th

International Workshop on Frontiers in Handwriting Recognition” (IWFHR „06), La Baule, France, 2006.

12. H. Gamboa, A. Fred, “An identity authentication system based on human computer interaction behavior, in:

3rd International Workshop on Pattern Recognition on Information Systems”, 2003, pp. 46–55.

13. S. Hashia, C. Pollett, M. Stamp, “On using mouse movements as a biometric, in: Proceeding in the

International Conference on Computer Science and its Applications”, vol. 1, 2005.

14. A.A.E. Ahmed, I. Traore, “A new biometric technology based on mouse dynamics, IEEE Transactions on

Dependable and Secure Computing” 4 (3) (2007) 165–179.

15. Maja Pusara, Carla E. Brodley, “User Re-Authentication via Mouse Movements”, SEC/DMSEC'04, October

29, 2004, Washington, DC, USA. Copyright 2004 ACM 1-58113-974-8/04/0010

47

9.2 Web References

[W1]. http://www.google.co.in/

[W2]. http://www.csharpcorner.com/

[W3]. http://www.stackoverflow.com/

[W4]. http://www.wikipedia.com/

[W5].http://www.codeproject.com/

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10. LIST OF JOURNALS AND PUBLICATIONS

1. “User Identity Verification Using Mouse Signature” in the

International Organization of Scientific Research (IOSR) e-ISSN:

2278-0661, p- ISSN: 2278-8727Volume 12, Issue 4 (Jul. - Aug.

2013).

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Thank You Very Much

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