  In recent years, machine learning algorithms have been applied widely in
various fields such as health, transportation, and the autonomous car. With the
rapid developments of deep learning techniques, it is critical to take the
security concern into account for the application of the algorithms. While
machine learning offers significant advantages in terms of the application of
algorithms, the issue of security is ignored. Since it has many applications in
the real world, security is a vital part of the algorithms. In this paper, we
have proposed a mitigation method for adversarial attacks against machine
learning models with an autoencoder model that is one of the generative ones.
The main idea behind adversarial attacks against machine learning models is to
produce erroneous results by manipulating trained models. We have also
presented the performance of autoencoder models to various attack methods from
deep neural networks to traditional algorithms by using different methods such
as non-targeted and targeted attacks to multi-class logistic regression, a fast
gradient sign method, a targeted fast gradient sign method and a basic
iterative method attack to neural networks for the MNIST dataset.
