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Lecture at ZISC by Marc Dacier from Symantec

Marc Dacier from symantec has presented a one hour lecture at the ZISC Information Security colloquium ( including pointers to WOMBAT.

In order to assure accuracy and realism of resilience assessment methods and tools, it is essential to have access to field data that are unbiased and representative. Several initiatives are taking place that offer access to malware samples for research purposes. Papers are published where techniques have been assessed thanks to these samples. Definition of benchmarking datasets is the next step ahead. In this presentation, we report on the lessons learned while collecting and analyzing malware samples in a large scale collaborative effort. Three different environments are described and their integration used to highlight the open issues that remain with such data collection. Three main lessons are offered to the reader. First, creation of representative malware samples datasets is probably an impossible task. Second, false negative alerts are not what we think they are. Third, false positive alerts exist where we were not used to see them. These three lessons have to be taken into account by those who want to assess the resilience of techniques with respect to malicious faults.

These are the results of a joint work carried out in the context of the European funded WOMBAT project, together with partners from Hispasec Systemas, EURECOM institute and Symantec Research Labs Europe (see for more on the WOMBAT project). Zurich_ZISC_presentation.pdf

WOMBAT paper accepted at NDSS2009

The following paper has been accepted at the Network and Distributed Systems Security (NDSS) 2009 conference:

Title: Scalable, Behavior-Based Malware Clustering
  • Ulrich Bayer, TUV
  • Paolo Milani Comparetti, TUV
  • Clemens Hlauschek, TUV
  • Christopher Kruegel, UCSB
  • Engin Kirda, Eurecom

Anti-malware companies receive thousands of malware samples every day. To process this large quantity, a number of automated analysis tools were developed. These tools execute a malicious program in a controlled environment and produce reports that summarize the program's actions. Of course, the problem of analyzing the reports still remains. Recently, researchers have started to explore automated clustering techniques that help to identify samples that exhibit similar behavior. This allows an analyst to discard reports of samples that have been seen before, while focusing on novel, interesting threats. Unfortunately, previous techniques do not scale well and frequently fail to generalize the observed activity well enough to recognize related malware.

In this paper, we propose a scalable clustering approach to identify and group malware samples that exhibit similar behavior. For this, we first perform dynamic analysis to obtain the execution traces of malware programs. These execution traces are then generalized into behavioral profiles, which characterize the activity of a program in more abstract terms. The profiles serve as input to an efficient clustering algorithm that allows us to handle sample sets that are an order of magnitude larger than previous approaches. We have applied our system to real-world malware collections. The results demonstrate that our technique is able to recognize and group malware programs that behave similarly, achieving a better precision than previous approaches. To underline the scalability of the system, we clustered a set of more than 75 thousand samples in less than three hours.