Efficient Methods to Trigger Adversarial Behaviors from Malware during Virtual Execution in SandBox

Abstract

Recently, various organizations are confronting a grater attack surface, the growing proliferation of malware and the number of malicious codes has been consistently growing for several years. To respond actively against these malicious codes, analysts employ automated investigation tools on the malware. However, there has been advent of malware employing the various techniques to avoid the detection of the SandBox, which makes hard to identify the adversarial behaviors of the samples codes. In this paper, we propose efficient methods to trigger adversarial behaviors from the sample codes during virtual execution in the Sandbox in order to perform the analysis of malware.