Hybrid Framework for Integrated Malware Analysis to Detect Advanced Persistent Threat

Abstract

The term Advanced Persistent Threat (APT) is used as a replacement term for cyber warfare and malware has developed into the major vehicle for APT. Malware analysis and detection is a major resource in maintaining an organization’s antivirus preparedness and responsiveness by contributing to the well-being of its IT health, and consequently to that of the economy as a whole. There is a need to develop an automatic malware detection and classification system to identify the variants of malware, in order to guide analysts in the selection of samples that require the most attention. In this paper, we introduce Hybrid Framework for our ongoing research, Integrated Malware Analysis to Detect Advanced Persistent Threat (APT). In our framework we integrated the static and dynamic malware analysis as well as K-mean clustering and Bayesian classification approaches.