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A framework to prevent insider threats by mitigating lunchtime attacks using biometrics

Tattur Nagaraja, Goutham (2021) A framework to prevent insider threats by mitigating lunchtime attacks using biometrics. Masters thesis, Dublin, National College of Ireland.

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Electronic devices are becoming increasingly crucial for essential activities such as e-commerce, e-voting, and e-banking as people rely more on them. Biometric authentication methods such as fingerprint verification and facial recognition are widely regarded as highly effective. In current systems, the user is only verified at the point of entry into the system and is not authenticated later. This may lead to unauthorized users gaining access to the system. This paper proposes that the user should be verified at frequent intervals as it becomes increasingly crucial to make sure the system is being accessed by an authorized user throughout. In this paper, we present a multi-authentication framework that combines fingerprinting verification and facial recognition as methods to authenticate authorized users. The proposal involves continuously verifying the biometrics of the user at regular intervals of time to be sure that the genuine user is accessing the system. The framework was built using Raspberry pi with python as the developing language with the help of a Pi camera and external fingerprint sensor. A dataset was used to train the raspberry pi using deep learning for accurate facial recognition and liveness detection resulting in capturing the user’s biometrics efficiently.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Tamara Malone
Date Deposited: 05 Jan 2023 16:23
Last Modified: 05 Jan 2023 16:23

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