  Deep learning algorithms have been recently targeted by attackers due to
their vulnerability. Several research studies have been conducted to address
this issue and build more robust deep learning models. Non-continuous deep
models are still not robust against adversarial, where most of the recent
studies have focused on developing attack techniques to evade the learning
process of the models. One of the main reasons behind the vulnerability of such
models is that a learning classifier is unable to slightly predict perturbed
samples. To address this issue, we propose a novel objective/loss function, the
so-called marginal contrastive, which enforces the features to lie under a
specified margin to facilitate their prediction using deep convolutional
networks (i.e., Char-CNN). Extensive experiments have been conducted on
continuous cases (e.g., UNSW NB15 dataset) and discrete ones (i.e,
eight-large-scale datasets [32]) to prove the effectiveness of the proposed
method. The results revealed that the regularization of the learning process
based on the proposed loss function can improve the performance of Char-CNN.
