Adversarial Machine Learning Mitigation: *Adversarial Learning*

There are several attacks against deep learning models in the literature, including fast-gradient sign method (FGSM), basic iterative method (BIM) or momentum iterative method (MIM) attacks. These attack are the purest form of the gradient-based evading technique that is used by attackers to evade the classification model. In this work, I will present a new approach to protect a malicious activity detection model from the several adversarial machine learning attacks. Hence, we explore the power of applying adversarial training to build a robust model against FGSM attacks. Accordingly, (1) dataset enhanced with the adversarial examples; (2) deep neural network-based detection model is trained using the KDDCUP99 dataset to learn the FGSM based attack patterns.