  The use of machine learning and intelligent systems has become an established
practice in the realm of malware detection and cyber threat prevention. In an
environment characterized by widespread accessibility and big data, the
feasibility of malware classification without the use of artificial
intelligence-based techniques has been diminished exponentially. Also
characteristic of the contemporary realm of automated, intelligent malware
detection is the threat of adversarial machine learning. Adversaries are
looking to target the underlying data and/or algorithm responsible for the
functionality of malware classification to map its behavior or corrupt its
functionality. The ends of such adversaries are bypassing the cyber security
measures and increasing malware effectiveness. The focus of this research is
the design of an intelligent systems approach using machine learning that can
accurately and robustly classify malware under adversarial conditions. Such an
outcome ultimately relies on increased flexibility and adaptability to build a
model robust enough to identify attacks on the underlying algorithm.
