Binary Function Clustering using Semantic Hashes
October 2014 • Article
Wesley Jin, Sagar Chaki, Cory Cohen, Arie Gurfinkel, Jeff Havrilla, Chuck Hines, Priya Narasimhan (Carnegie Mellon University)
In this article, the authors present an alternative to pair wise comparisons based on "hashing” that captures the semantics of functions as semantic hashes.
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 pair wise comparisons (e.g., template matching using SMT solvers, Value-Set analysis at critical program points, API call matching, etc.) However, these methods are unshakable 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 Min Hashing, 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.