Talks and Poster Presentations (with Proceedings-Entry):

G. Jacob, P. Milani Comparetti, M. Neugschwandtner, C. Krügel, G. Vigna:
"A Static, Packer-Agnostic Filter to Detect Similar Malware Sample";
Talk: Conference on Detection of Intrusions and Malware and Vulnerability Assessment (DIMVA), Heraklion, Kreta; 2012-07-26 - 2012-07-27; in: "Proceedings of the 9th Conference on Detection of Intrusions and Malware & Vulnerability Assessment", Springer, (2012).

English abstract:
The steadily increasing number of malware variants is a significant problem, clogging the input queues of automated analysis tools. The generation of malware variants is made easy by automatic packers and polymorphic engines, which produce by encryption and compression a multitude of distinct versions. A great deal of time and resources could be saved by prioritizing samples to analyze, either, to avoid the repeated analyses of variants and focus on innovative malware, or, on the con- trary, to re-analyze variants and have better insights on their evolution. Unfortunately, indexing in malware analysis tools and repositories relies on executable digests (hashes) that strongly differ for each variant.
In this paper, we present a robust filter to quickly determine when a malware program is similar to a previously-seen sample. Compared to previous work, our similarity measure does not require the costly task of preliminary unpacking, but instead, operates directly on packed code. Our approach exploits the fact that current packers use compression and weak encryption schemes that do not break, in the packed versions, all the similarities existing between the original versions of two programs. In addition, we introduce a packer detection technique that is able to distinguish between different levels of protection, such as unpacked, compressed, encrypted, and multi-layer encrypted code. This allows us to optimize the sensitivity of the similarity measure accordingly. We evaluated our approach on a large malware repository containing 795,000 samples. Our results show that the similarity measure is highly effective in filtering out malware variants, even after re-packing, and can reduce the number of samples that need to be analyzed by a factor of 3 to 5.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.