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Razaque, A., Amsaad, F., Almiani, M., Gulsezim, D., Almahameed, M. A., Al-Dmour, A., Khan, M. J., Ganda, R..  2020.  Successes and Failures in Exploring Biometric Algorithms in NIST Open Source Software and Data. 2020 Seventh International Conference on Software Defined Systems (SDS). :231—234.

With the emergence of advanced technology, the user authentication methods have also been improved. Authenticating the user, several secure and efficient approaches have been introduced, but the biometric authentication method is considered much safer as compared to password-driven methods. In this paper, we explore the risks, concerns, and methods by installing well-known open-source software used in Unibiometric analysis by the partners of The National Institute of Standards and Technology (NIST). Not only are the algorithms used all open source but it comes with test data and several internal open source utilities necessary to process biometric data.

Pundir, N., Hazari, N. A., Amsaad, F., Niamat, M..  2017.  A Novel Hybrid Delay Based Physical Unclonable Function Immune to Machine Learning Attacks. 2017 IEEE National Aerospace and Electronics Conference (NAECON). :84–87.

In this paper, machine learning attacks are performed on a novel hybrid delay based Arbiter Ring Oscillator PUF (AROPUF). The AROPUF exhibits improved results when compared to traditional Arbiter Physical Unclonable Function (APUF). The challenge-response pairs (CRPs) from both PUFs are fed to the multilayered perceptron model (MLP) with one hidden layer. The results show that the CRPs generated from the proposed AROPUF has more training and prediction errors when compared to the APUF, thus making it more difficult for the adversary to predict the CRPs.