  Malware has been one of the most damaging threats to computers that span
across multiple operating systems and various file formats. To defend against
ever-increasing and ever-evolving malware, tremendous efforts have been made to
propose a variety of malware detection that attempt to effectively and
efficiently detect malware so as to mitigate possible damages as early as
possible. Recent studies have shown that, on the one hand, existing ML and DL
techniques enable superior solutions in detecting newly emerging and previously
unseen malware. However, on the other hand, ML and DL models are inherently
vulnerable to adversarial attacks in the form of adversarial examples. In this
paper, we focus on malware with the file format of portable executable (PE) in
the family of Windows operating systems, namely Windows PE malware, as a
representative case to study the adversarial attack methods in such adversarial
settings. To be specific, we start by first outlining the general learning
framework of Windows PE malware detection based on ML/DL and subsequently
highlighting three unique challenges of performing adversarial attacks in the
context of Windows PE malware. Then, we conduct a comprehensive and systematic
review to categorize the state-of-the-art adversarial attacks against PE
malware detection, as well as corresponding defenses to increase the robustness
of Windows PE malware detection. Finally, we conclude the paper by first
presenting other related attacks against Windows PE malware detection beyond
the adversarial attacks and then shedding light on future research directions
and opportunities. In addition, a curated resource list of adversarial attacks
and defenses for Windows PE malware detection is also available at
https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection.
