  With the increasingly rapid development of new malicious computer software by
bad faith actors, both commercial and research-oriented antivirus detectors
have come to make greater use of machine learning tactics to identify such
malware as harmful before end users are exposed to their effects. This, in
turn, has spurred the development of tools that allow for known malware to be
manipulated such that they can evade being classified as dangerous by these
machine learning-based detectors, while retaining their malicious
functionality. These manipulations function by applying a set of changes that
can be made to Windows programs that result in a different file structure and
signature without altering the software's capabilities. Various proposals have
been made for the most effective way of applying these alterations to input
malware to deceive static malware detectors; the purpose of this research is to
examine these proposals and test their implementations to determine which
tactics tend to generate the most successful attacks.
