  In the past decades, the rise of artificial intelligence has given us the
capabilities to solve the most challenging problems in our day-to-day lives,
such as cancer prediction and autonomous navigation. However, these
applications might not be reliable if not secured against adversarial attacks.
In addition, recent works demonstrated that some adversarial examples are
transferable across different models. Therefore, it is crucial to avoid such
transferability via robust models that resist adversarial manipulations. In
this paper, we propose a feature randomization-based approach that resists
eight adversarial attacks targeting deep learning models in the testing phase.
Our novel approach consists of changing the training strategy in the target
network classifier and selecting random feature samples. We consider the
attacker with a Limited-Knowledge and Semi-Knowledge conditions to undertake
the most prevalent types of adversarial attacks. We evaluate the robustness of
our approach using the well-known UNSW-NB15 datasets that include realistic and
synthetic attacks. Afterward, we demonstrate that our strategy outperforms the
existing state-of-the-art approach, such as the Most Powerful Attack, which
consists of fine-tuning the network model against specific adversarial attacks.
Finally, our experimental results show that our methodology can secure the
target network and resists adversarial attack transferability by over 60%.
