  Deep neural networks (DNNs) have proven to be quite effective in a vast array
of machine learning tasks, with recent examples in cyber security and
autonomous vehicles. Despite the superior performance of DNNs in these
applications, it has been recently shown that these models are susceptible to a
particular type of attack that exploits a fundamental flaw in their design.
This attack consists of generating particular synthetic examples referred to as
adversarial samples. These samples are constructed by slightly manipulating
real data-points in order to "fool" the original DNN model, forcing it to
mis-classify previously correctly classified samples with high confidence.
Addressing this flaw in the model is essential if DNNs are to be used in
critical applications such as those in cyber security.
  Previous work has provided various learning algorithms to enhance the
robustness of DNN models, and they all fall into the tactic of "security
through obscurity". This means security can be guaranteed only if one can
obscure the learning algorithms from adversaries. Once the learning technique
is disclosed, DNNs protected by these defense mechanisms are still susceptible
to adversarial samples. In this work, we investigate this issue shared across
previous research work and propose a generic approach to escalate a DNN's
resistance to adversarial samples. More specifically, our approach integrates a
data transformation module with a DNN, making it robust even if we reveal the
underlying learning algorithm. To demonstrate the generality of our proposed
approach and its potential for handling cyber security applications, we
evaluate our method and several other existing solutions on datasets publicly
available. Our results indicate that our approach typically provides superior
classification performance and resistance in comparison with state-of-art
solutions.
