  We propose a novel method to capture data points near decision boundary in
neural network that are often referred to a specific type of uncertainty. In
our approach, we sought to perform uncertainty estimation based on the idea of
adversarial attack method. In this paper, uncertainty estimates are derived
from the input perturbations, unlike previous studies that provide
perturbations on the model's parameters as in Bayesian approach. We are able to
produce uncertainty with couple of perturbations on the inputs. Interestingly,
we apply the proposed method to datasets derived from blockchain. We compare
the performance of model uncertainty with the most recent uncertainty methods.
We show that the proposed method has revealed a significant outperformance over
other methods and provided less risk to capture model uncertainty in machine
learning.
