  The ever-growing big data and emerging artificial intelligence (AI) demand
the use of machine learning (ML) and deep learning (DL) methods. Cybersecurity
also benefits from ML and DL methods for various types of applications. These
methods however are susceptible to security attacks. The adversaries can
exploit the training and testing data of the learning models or can explore the
workings of those models for launching advanced future attacks. The topic of
adversarial security attacks and perturbations within the ML and DL domains is
a recent exploration and a great interest is expressed by the security
researchers and practitioners. The literature covers different adversarial
security attacks and perturbations on ML and DL methods and those have their
own presentation styles and merits. A need to review and consolidate knowledge
that is comprehending of this increasingly focused and growing topic of
research; however, is the current demand of the research communities. In this
review paper, we specifically aim to target new researchers in the
cybersecurity domain who may seek to acquire some basic knowledge on the
machine learning and deep learning models and algorithms, as well as some of
the relevant adversarial security attacks and perturbations.
