Support vector machines for program analysis

The prerequisite for practicable program analysis is the identification of the individual procedures, which correspond to individual stack frames. We present how machine learning techniques can be used in the setting of program analysis in order to find these stack frames. This combination of machine learning and abstract interpretation-based analysis provides the first fully automatic analysis framework for executables. Our approach can also be applied to identify library functions or malicious behaviour in a given piece of assembly.