An Empirical Analysis of Ensemble Systems in Cancellable Behavioural Biometrics: a Touch Screen Dataset

An Empirical Analysis of Ensemble Systems inCancellable Behavioural Biometrics: a Touch Screen

Dataset

Marcelo Damasceno1,2 A.M.P. Canuto2

1Federal Institute of Education, Science and Technology of Rio Grande do Norte - São Gonçalo doAmarante

2Federal University of Rio Grande do Norte

12/05/2013

Marcelo Damasceno (IFRN) An Empirical Analysis of Ensemble Systems in Cancellable Behavioural Biometrics: a Touch Screen Dataset12/05/2013 1 / 36

About

This paper analyzes the performance of ensemble systems in the contextof cancellable behavioural biometrics, more specifically a touch-screendataset.

The main aim of this work is to analyse the gain that the use of ensemblesystems in cancellable data can bring with respect to the behaviouralbiometric context.


Outline

1 Introduction

2 Cancellable Transformations

3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results

7 Conclusion and Further Work

8 References


Introduction

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


Introduction

User Verification

Currently most computer systems use individual username and passwordto authenticate their users [1];

Username-password method brings some problems as the use of sameusername and password for different services on the Internet and thestress to remember secure, long and complex passwords;


Introduction

Biometrics

Biometrics can be considered as the science of establishing the identityof a person using his/her anatomical and/or behavioural traits.

Biometric traits have a number of desirable properties, such as reliability,convenience, universality, and so forth.

Because of these characteristics, biometrics has been increasinglydeveloped over the last years.


Introduction

Behavioural Biometrics

Unlike physical biometrics, behavioural biometrics are related to user be-haviour/actions [3].

These biometrics use behavioural patterns, such as gait, typing or theway in which a user uses a computer system.

The behavioural biometrics is non-intrusive, i.e, often informationcollection is not perceived by users.


Introduction

Biometrics Problems

The biometric is permanently associated with a user and cannot berevoked or cancelled if compromised [4].

If a biometric identifier is compromised, it is lost forever and possibly thesame happens for every application where the biometric is used.

The use of cancellable biometrics is being increasingly adopted toaddress such security issues.


Introduction

Cancellable Biometrics

This approach uses transformed or intentionally-distorted biometric datainstead of original biometric data for authentication [5, 6].

There is a risk that using such transformed data will decrease theperformance of the biometric-based system.


Cancellable Transformations

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References



Cancellable Transformation

The non-invertible transformation functions can transform the biometricdata in a way that it is computationally impossible to get the original form;

The distorted data brings some undesired consequences as highvariance, what makes more difficult the users identification;



Transformation Functions

1 Interpolation: Based on polynomial interpolations;2 BioHashing: Characterized by transforming the original biometric into a

non-invertible binary sequence;3 BioConvolving: The transformed functions are created through linear

combinations of sub-parts of the original biometric template;4 DoubleSum: Consists of summing the attributes of the original biometric

model with two other attributes of the same sample;


Ensemble Systems

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


Ensemble Systems

Ensemble Systems

These systems exploit the idea that different classifiers can offercomplementary information about patterns [7].

Figure 1 presents a general structure of an ensemble system, which iscomposed of a set of N individual classifiers (ICn), organized in a parallelway.


Ensemble Systems

Ensemble Systems

Figure : An illustration of the general framework of an ensemble system


TouchAnalytics

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


TouchAnalytics

TouchAnalytics

The behavioural biometric modality used in this work is a touch screendata, which represents a combination of strokes collected fromsmartphones.

TouchAnalytics, was collected by Frank et al. [2]. They inform how thedata was collected, processed and some initial results


TouchAnalytics

TouchAnalytics

This dataset is composed of 30 attributes and all the attributes arederived from the strokes obtained from 41 users.

Strokes are composed of horizontal and scrolling (vertical) movements.

The dataset was binarized because we use a verification process. It wascreated a different dataset for each user.


TouchAnalytics

TouchAnalytics Scenarios

1 Inter Session: The goal is to authenticate users across multiple sessionsperformed in the same day.

2 Inter Week: The goal is to authenticate users after in two different weeks(the period of time between these two sessions is one week).

3 Intra Session: All the user data was used in the process, timeindependently. In this scenario, we used a 10 fold cross-validationprocess.


TouchAnalytics

TouchAnalytics Results

According to [8], the mean EER:

Intra Session are 0%: Within one session, most users do notconsiderably change their touch behaviour;

Inter Session: 2% to 3%

Inter Week: 0% to 4%

This result indicates the behavioural biometrics (touch data) has goodperspectives in practical use.


Methodology

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


Methodology

Methodology I I

This investigation will use only the Intra Session experiment.

The use of three ensemble structures is analysed:Bagging, Stacking andMajority Voting

We applied two different ensemble sizes in Bagging: six and twelveindividual classifiers.

Stacking and Voting use 6 individual classifiers in their structure.


Methodology

Methodology II

k -NN and Logistic Regression was selected as combination methods forensembles generated by Stacking.

We use SVM and k -NN as individual classifiers in half-by-half proportionin heterogeneous structures (Bagging and Voting).

The 10-fold cross-validation methodology was used in empirical analysis.

The Mann-Whitney statistical test with the confidence level is 95%(α = 0.05) is applied to compare the s EER of ensemble systems appliedin cancellable data versions with the EER achieved in original data.


Results

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


Results

Results and Discussion - Scrooling

Table : Median Results using Scrooling Traits

Method Original Interpolation BioHashing BioConvol. Double SumBag_6_k 7.6±4.8 8.9±5.4 32.3±12.6 3.3±10.7 8.7±5.5Bag_12_k 7.4±4.9 8.6±5.1 32.4±12.4 3.2±10.8 8.6±5.4Bag_6_S 9.2±6.4 12.4±8.2 32.4±19 2.3±7.8 11.9±8.1Bag_12_S 9.2±6.4 12.3±8 31.2±15.5 2.1±7.8 11.7±8.3Stack_kSk 7.8±5.1 10±6.3 32.3±13.1 3.4±10.6 10±6.5Stack_kSL 7.2±4.7 9±5.5 32.7±12.7 3.4±10.9 9.1±5.8

Voting 8.9±6.4 10.9±6.7 32.6±12.6 3.6±11 11.4±7.5

Shaded cells are statistically similar.

Bold values mean that the cancellable result was statistically better.


Results

Results and Discussion - Horizontal Strokes

Table : Median Results using Horizontal Traits

Method Original Interpolation BioHashing BioConvol. Double SumBag_6_k 8±4.5 10.6±6.3 32.8±9.8 0.1±0.3 8.7±4.8Bag_12_k 7.7±4.3 10.3±6.2 32.8±10.2 0.1±0.3 8.6±4.6Bag_6_S 11.1±7.3 16.1±9 34.4±17.7 0.4±0.5 13.3±8.4

Bag_12_S 11.7±7.8 16.1±9.2 33.5±26 0.3±0.4 13.1±8.5Stack_kSk 8.5±4.9 12.1±7.3 34±9.5 0.2±0.4 10.7±6.3Stack_kSL 7.7±4.5 10.8±6.7 33.1±10 0.2±0.4 9.6±5.4

Voting 9.7±5.8 13.7±7.1 33.5±9.7 0.2±0.4 11.9±6.9


Results

Discussion I

Ensembles used in Interpolation and Double Sum data have similarstatistical results when compared with results achieved by the Originaldatasets (Tables 1 and 2).

EER values in BioConvolving are statistical better than EER from Originaldata, for both strokes directions and for all ensemble structures (Tables 1and 2).

The use of ensemble systems in behavioural cancellable biometrics donot deteriorate the EER results, comparing with the EER achieved byOriginal data.

Our only exception was BioHashing transformation that achieves theworst EER values, compared with all ensemble structures.

The use of ensemble structures improves the results using Interpolation,BioConvolving and Double Sum functions in scrolling strokes comparedwith results achieved in our previous work [2].


Results

Discussion II

These results show that we can use ensemble systems and cancellabletransformation in behavioural biometrics instead of the original data,without deteriorating the performance of the biometric-basedauthentication systems.


Conclusion and Further Work

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References



Conclusion I

In this paper, we performed a comparative analysis of well-knownensemble structures applied to cancellable behavioural biometrics.

Four cancellable functions (Interpo- lation, BioHashing, BioConvolvingand Double Sum) were applied in this dataset to demonstrate theimportance and perspectives of cancellable behavioural biometrics.

The Interpolation and Double Sum results were statistical similar toOriginal results. The mean EER of Original dataset varies from 7.4% to11.7%, while in Interpolation dataset, the EER varies between 8.6% and16.1%.

Double Sum dataset, EER varies from 8.6% to 13.3%.

The mean ERR of BioConvolving dataset varies from 0.1% and 3.60%,and it was statistically superior than all other datasets, for all ensemblesstructures.



Conclusion II

The results obtained by BioConvolving are promising and indicate that theuse of cancellable behavioural biometrics can have a positive effect inbiometric-based authentication systems.

The results achieved in this paper are better than in our previous [2]. Thisshows that the use of ensembles methods are better than using singleclassifiers.

We have demonstrated that the use of a transformation function usuallyprovides similar or better performance than the original biometric data

As a future work, in order to improve the results we will use moreclassifiers as MultiLayer Perceptrons, optimize cancelable functionparameters and use of multimodal biometrics.


References

Outline

1 Introduction


3 Ensemble Systems

4 TouchAnalytics

5 Methodology

6 Results


8 References


References

References I

W. Jackson, “Antisec hackers claim theft of military e-mails from boozallen,” Internet, Julho 2011, acessado em Novembro de 2011. [Online].Available: http://gcn.com/articles/2011/07/11/antisec-booz-allen-hack-military-emails.aspx

M. Damasceno, A. M. P. Canuto, An Empirical Analysis of CancellableTransformations in a Behavioral Biometric Modality. In: IEEE. 13thConference on Hybrid Intelligent Systems. Tunis, Tunisia: IEEE, 2013. Inpress.

K. Revett, Behavioral Biometrics: a Remote Access Approach. JohnWiley & Sons, Ltd, 2008.

A. K. Jain, K. Nandakumar, and A. Nagar, “Biometric template security,” inEURASIP Journal On Advances in Signal Processing, 2008.


http://gcn.com/articles/2011/07/11/antisec-booz-allen-hack-military-emails.aspx

http://gcn.com/articles/2011/07/11/antisec-booz-allen-hack-military-emails.aspx

References

References II

C. Lee and J. Kim, “Cancelable fingerprint templates usingminutiae-based bit-strings,” Journal of Network and ComputerApplications, vol. 33, no. 3, pp. 236 – 246, 2010.

A. Nagar, K. Nandakumar, and A. K. Jain, “A hybrid biometriccryptosystem for securing fingerprint minutiae templates,” Pattern Recogn.Lett., vol. 31, pp. 733–741, June 2010.

A. M. P. Canuto, M. Abreu, L. Oliveira, J. C. X. Jr., and A. Santos,“Investigating the influence of the choice of the ensemble members inaccuracy and diversity of selection-based and fusion-based methods forensembles,” Patt Recogn Letters, vol. 28, no. 4, pp. 472–486, 2007.


References

References III

M. Frank, R. Biedert, E. Ma, I. Martinovic, and D. Song, “Touchalytics: Onthe Applicability of Touchscreen Input as a Behavioral Biometric forContinuous Authentication,” in IEEE Transactions on InformationForensics and Security, vol. 8, no. 1, 2013, pp. 136–148. [Online].Available: http://www.mariofrank.net/touchalytics/


http://www.mariofrank.net/touchalytics/

References

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An Empirical Analysis of Ensemble Systems in Cancellable Behavioural Biometrics: a Touch Screen Dataset