The use of machine learning and intelligent systems has become an established\npractice in the realm of malware detection and cyber threat prevention. In an\nenvironment characterized by widespread accessibility and big data, the\nfeasibility of malware classification without the use of artificial\nintelligence-based techniques has been diminished exponentially. Also\ncharacteristic of the contemporary realm of automated, intelligent malware\ndetection is the threat of adversarial machine learning. Adversaries are\nlooking to target the underlying data and/or algorithm responsible for the\nfunctionality of malware classification to map its behavior or corrupt its\nfunctionality. The ends of such adversaries are bypassing the cyber security\nmeasures and increasing malware effectiveness. The focus of this research is\nthe design of an intelligent systems approach using machine learning that can\naccurately and robustly classify malware under adversarial conditions. Such an\noutcome ultimately relies on increased flexibility and adaptability to build a\nmodel robust enough to identify attacks on the underlying algorithm.\n