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Biometrics in a Data Flow Context with Immune Algorithms

Grant number: 12/25032-0
Support type:Scholarships in Brazil - Doctorate
Effective date (Start): June 01, 2013
Effective date (End): February 28, 2017
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
Principal Investigator:André Carlos Ponce de Leon Ferreira de Carvalho
Grantee:Paulo Henrique Pisani
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil

Abstract

The growing presence of the Internet day-to-day tasks, together with the evolution of the computing systems, led to a greater data exposure. This scenario highlights the need for safer user authentication systems. A promising approach for user authentication is the use of biometric technologies, which analysis user's physiological and behavioral features. Biometric technologies have less vulnerabilities than passwords or cards for authentication, which may be stolen or even cloned. An important feature of biometric data is that they may undergo small changes through time for the same user. Due to that, classifiers which adopt a static approach may reduce their predictive performance through time, as they would not adapt to these changes. In machine learning, an area which involves related concepts is data flow mining. The mentioned phenomena is known as concept drift in the area of data flow mining. It is possible to draw a parallel between the user profile modeling with biometrics and the functioning of the artificial immune systems, a subarea of computational intelligence widely used in machine learning. Both need to identify what is normal in order to find deviations, which would be potential attacks. This parallel shows that the application of immune algorithms is a promising alternative for the user recognition by biometric means. In fact, these algorithms attained good performance in previous works in the area. However, an aspect which has been little explored is the study of these algorithms in a scenario which concept drift occurs, as mentioned. The proposal of this work is to combine concepts of data flow mining with immune algorithms for the user profile modeling with biometrics, considering the fact that concept drift may arise.

Scientific publications (4)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
PISANI, PAULO HENRIQUE; MHENNI, ABIR; GIOT, ROMAIN; CHERRIER, ESTELLE; POH, NORMAN; DE LEON FERREIRA DE CARVALHO, ANDRE CARLOS PONCE; ROSENBERGER, CHRISTOPHE; BEN AMARA, NAJOUA ESSOUKRI. Adaptive Biometric Systems: Review and Perspectives. ACM COMPUTING SURVEYS, v. 52, n. 5 OCT 2019. Web of Science Citations: 0.
PISANI, PAULO HENRIQUE; LORENA, ANA CAROLINA; DE CARVALHO, ANDRE C. P. L. F. Adaptive Biometric Systems Using Ensembles. IEEE INTELLIGENT SYSTEMS, v. 33, n. 2, p. 19-28, MAR-APR 2018. Web of Science Citations: 1.
PISANI, PAULO HENRIQUE; POH, NORMAN; DE CARVALHO, ANDRE C. P. L. F.; LORENA, ANA CAROLINA. Score normalization applied to adaptive biometric systems. COMPUTERS & SECURITY, v. 70, p. 565-580, SEP 2017. Web of Science Citations: 2.
PISANI, PAULO HENRIQUE; LORENA, ANA CAROLINA; DE CARVALHO, ANDRE C. P. L. F. Adaptive algorithms applied to accelerometer biometrics in a data stream context. Intelligent Data Analysis, v. 21, n. 2, p. 353-370, 2017. Web of Science Citations: 3.
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
PISANI, Paulo Henrique. Biometrics in a data stream context. 2017. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação São Carlos.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.