Classification of malware based on integrated static and dynamic features
Collection of dynamic information requires that malware be executed in a controlled environment; the malware unpacks itself as a preliminary to the execution process. On the other hand, while execution of malware is not needed in order to collect static information, the file must first be unpacked manually. None-the-less, if a file has been executed, it is possible to use both static and dynamic information in designing a single classification method. In this paper, we present the first classification method integrating static and dynamic features into a single test. Our approach improves on previous results based on individual features and reduces by half the time needed to test such features separately. Robustness to changes in malware development is tested by comparing results on two sets of malware, the first collected between 2003 and 2007, and the second collected between 2009 and 2010. When classifying the older set as compared to the entire data set, our integrated test demonstrates significantly more robustness than previous methods by losing just 2.7% in accuracy as opposed to a drop of 7%. We conclude that to achieve acceptable accuracy in classifying the latest malware, some older malware should be included in the set of data.
dynamic feature malware classification static feature