Binary Function Clustering using Semantic Hashes
Wesley Jin, Sagar Chaki, Cory Cohen, Arie Gurfinkel, Jeffrey Havrilla, Charles Hines, Priya Narasimhan, Proceedings of the 11th International Conference on Machine Learning and Applications (ICMLA), page 386--391, December 12--15, 2012.
Abstract: The ability to identify semantically-related functions, in large collections of binary executables, is important for malware detection. Intuitively, two pieces of code are similar if they have the same effect on a machine's state. Current state-of-the-art tools employ a variety of pairwise comparisons (e.g., template matching using SMT solvers, Value-Set analysis at critical program points, API call matching, etc.) However, these methods are unscalable for clustering large datasets, of size N, since they require O(N^2) comparisons. In this paper, we present an alternative approach based upon "hashing". We propose a scheme that captures the semantics of functions as semantic hashes. Our approach treats a function as a set of features, each of which represent the input-output behavior of a basic block. Using a form of locality-sensitive hashing known as MinHashing, functions with many common features can be quickly identified, and the complexity of clustering is reduced to O(N). Experiments on functions extracted from the CERT malware catalog indicate that we are able to cluster closely-related code with a low false positive rate.