Biometric Authentication Systems (BAS) have several security benefits over traditional password and token authentication including an inherent difficulty to copy, clone and share or distribute authentication credentials (biometric traits). Spoofing or presentation attack remains a major weakness of biometric systems and tackling it at the trait level is still challenging with several different approaches and methods applied in existing systems. In this paper, we focus on the well-known approach of Suspicious Presentation Detection (SPD) and present the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) that further mitigates spoofing or presentations attacks using randomization and combination of several different SPD detection techniques across three different modalities during trait capture. We discuss the detection of life using five distinct properties each from finger, face and eye modalities and present results from a simulation that highlights the improved security based on an impostor's inability to accurately predict the combination of trait liveness properties the system might prompt and test for during capture.
Despite their advantages over password-based and token-based authentication, Biometric Authentication Systems (BAS) are not perfect. They are particularly vulnerable to spoofing, also called Suspicious Presentation (SP) attacks whereby an impostor presents a fake trait to the biometric scanner during verification. Spoofing has a critical impact on system security leading to a trust deficit on biometric systems with weak anti-spoofing mechanisms. Mitigating biometric spoofing is a possibility, hence several techniques have evolved in recent times including multi-biometrics, biometric cryptography and Liveness Detection (LD) -also called Suspicious Presentation Detection (SPD). Unfortunately, nearly all known LD techniques exhibit a fundamental set of flawsthey are mostly uni-modal, easily predictable by a well-equipped impostor, and can be circumvented by well-crafted SP attacks. This paper presents the Multi-Modal Random Trait Biometric Liveness Detection System (MMRTBLDS) framework, as an alternative approach that implements LD using multiple traits each acquired from separate modalities of the same subject combined in a randomized manner. The strength of the framework lays in the impostor's inability to accurately predict the exact set of randomized trait parameter combinations in advance of LD. The framework employs a 3D simulation of fifteen liveness parameters, composed of three each from finger, face and iris traits, based on random number generation. Simulation results obtained using 125 distinct randomized combinations show significant improvements in biometric authentication security with a system efficiency of 99.2%.
This research work aims at modelling a framework for Private Cloud infrastructure Deployment for Information and Communication Technology Centres (ICTs) in tertiary institutions in Nigeria. Recent researches have indicated that cloud computing will become the mainstream in computing technology and very effective for businesses. All Tertiary Institutions have ICT units, and are generally charged with the responsibilities of deploying ICT infrastructure and services for administration, teaching, research and learning in higher institution at large. The Structured System Analysis and Design Methodology (SSADM) is used in this research and a six-step framework for a cost effective and scalable Private cloud infrastructure using server virtualization is presented as an alternative that can guarantee total and independent control of data flow in the institutions, while ensuring adequate security of vital information.
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