Impact of Loss Function Using M-LSTM Classifier for Sequence Data
DOI:
https://doi.org/10.61841/kbeqd452Keywords:
Long Short-Term Memory (LSTM), Loss Function, Cross-Entropy, Hinge, Normalisation, Biometric, Key Stroke Dynamics (KSD)Abstract
The increasing dependence on information systems has opened a lot of possibilities in solving real life problems and led to the increase of threat to privacy, integrity and authentication. Even though a lot of key based authentication systems are in use biometrics provide a better performance, apart from physiological biometrics like iris, thumb impression etc., For verifying a person, a behavioural biometric technique called Keystroke dynamics can be used. Biometric based user authentication is a sequence classification task. This study provides a comparison of different loss functions and their performance on keystroke dynamics data. This work uses Long Short-Term Memory (LSTM) representing Neural Network and we have taken 5 different loss functions for the study.
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