Over the past years, we have experienced an increase in the quantity and complexity of malware binaries. This change has been fueled by the introduction of malware generation tools and reuse of different malcode modules. Recent malware appears to be highly modular and less functionally typified. A side-effect of this "composition" of components across different malware types, a growing number of new malware samples cannot be explicitly assigned to traditional classes defined by Anti-Virus (AV) vendors. Indeed, by nature, clustering techniques capture dominant behavior that could be a manifestation of only one of the malware component failing to reveal malware similarities that depend on other, less dominant components and other evolutionary traits. In this paper, we introduce a novel malware behavioral commonality analysis scheme that takes into consideration component-wise grouping, called behavioral mapping. Our effort attempts to shed light to malware behavioral relationships and go beyond simply clustering the malware into a family. To this end, we implemented a method for identifying soft clusters and reveal shared malware components and traits. Using our method, we demonstrate that a malware sample can belong to several groups (clusters), implying sharing of its respective components with other samples from the groups. We performed experiments with a large corpus of real-world malware data-sets and identified that we can successfully highlight malware component relationships across the existing AV malware families and variants. © 2012 Springer-Verlag Berlin Heidelberg.
Yavvari, C., Tokhtabayev, A., Rangwala, H., & Stavrou, A. (2012). Malware characterization using behavioral components. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7531 LNCS, pp. 226–239). Springer Verlag. https://doi.org/10.1007/978-3-642-33704-8_20