  Machine learning has witnessed tremendous growth in its adoption and
advancement in the last decade. The evolution of machine learning from
traditional algorithms to modern deep learning architectures has shaped the way
today's technology functions. Its unprecedented ability to discover
knowledge/patterns from unstructured data and automate the decision-making
process led to its application in wide domains. High flying machine learning
arena has been recently pegged back by the introduction of adversarial attacks.
Adversaries are able to modify data, maximizing the classification error of the
models. The discovery of blind spots in machine learning models has been
exploited by adversarial attackers by generating subtle intentional
perturbations in test samples. Increasing dependency on data has paved the
blueprint for ever-high incentives to camouflage machine learning models. To
cope with probable catastrophic consequences in the future, continuous research
is required to find vulnerabilities in form of adversarial and design remedies
in systems. This survey aims at providing the encyclopedic introduction to
adversarial attacks that are carried out against malware detection systems. The
paper will introduce various machine learning techniques used to generate
adversarial and explain the structure of target files. The survey will also
model the threat posed by the adversary and followed by brief descriptions of
widely accepted adversarial algorithms. Work will provide a taxonomy of
adversarial evasion attacks on the basis of attack domain and adversarial
generation techniques. Adversarial evasion attacks carried out against malware
detectors will be discussed briefly under each taxonomical headings and
compared with concomitant researches. Analyzing the current research challenges
in an adversarial generation, the survey will conclude by pinpointing the open
future research directions.
