Malware is a foundational component of cyber crime that enables an attacker to modify the normal operation of a computer or access sensitive, digital information. Despite the extensive research performed to identify such programs, existing schemes fail to detect evasive malware, an increasingly popular class of malware that can alter its behavior at run-time, making it difficult to detect using today’s state of the art malware analysis systems. In this thesis, we present DVasion, a comprehensive strategy that exposes such evasive behavior through a multi-execution technique. DVasion successfully detects behavior that would have been missed by traditional, single-execution approaches, while addressing the limitations of previously proposed multi-execution systems. We demonstrate the accuracy of our system through strong parallels with existing work on evasive malware, as well as uncover the hidden behavior within 167 of 1,000 samples.