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The use of machine learning and intelligent systems has become an establishedpractice in the realm of malware detection and cyber threat prevention. In anenvironment characterized by widespread accessibility and big data, thefeasibility of malware classification without the use of artificialintelligence-based techniques has been diminished exponentially. Alsocharacteristic of the contemporary realm of automated, intelligent malwaredetection is the threat of adversarial machine learning. Adversaries arelooking to target the underlying data and/or algorithm responsible for thefunctionality of malware classification to map its behavior or corrupt itsfunctionality. The ends of such adversaries are bypassing the cyber securitymeasures and increasing malware effectiveness. The focus of this research isthe design of an intelligent systems approach using machine learning that canaccurately and robustly classify malware under adversarial conditions. Such anoutcome ultimately relies on increased flexibility and adaptability to build amodel robust enough to identify attacks on the underlying algorithm.

Intelligent Systems Design for Malware Classification Under Adversarial Conditions