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Malicious code (malware) is used to steal sensitive data, to attack corporate networks, and to deliver spam. To silently compromise systems and maintain their access, malware developers usually apply obfuscation techniques that result in a massive amount of malware variants and that can render static analysis approaches ineffective. To address the limitations of static approaches, researchers have proposed dynamic analysis systems. These systems usually rely on a sandboxing environment that captures the system calls performed by a program under analysis. In this paper, the authors propose a novel approach to capture and model malware behavior that is based on the monitoring of the data values that a certain subset of instructions writes to memory during program execution.
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