Extraction of Statistically Significant Malware Behaviors

Traditionally, analysis of malicious software is only a semi-automated process, often requiring a skilled human analyst. As new malware appears at an increasingly alarming rate - now over 100 thousand new variants each day - there is a need for automated techniques for identifying suspicious behavior in programs.

In this paper, we propose a method for extracting statistically significant malicious behaviors from a system call dependency graph (obtained by running a binary executable in a sandbox). Our approach is based on a new method for measuring the statistical significance of subgraphs. Given a training set of graphs from two classes (e.g., goodware and malware system call dependency graphs), our method can assign p-values to subgraphs of new graph instances even if those subgraphs have not appeared before in the training data (thus possibly capturing new behaviors or disguised versions of existing behaviors).

Author(s):

Sirinda Palahan    
Penn State University
United States

Domagoj Babic    
Google, Inc.
United States

Swarat Chaudhuri    
Rice University
United States

Daniel Kifer    
Penn State University
United States