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Malware analysts are more and more overstrained by today´s flood of malware. Hence, the need for automated tools to assist malware analysis becomes apparent. A common approach of malware analysis is reverse engineering its components, namely (sub-)procedures. We present and evaluate simple procedure characteristics such as the number of instructions for their robustness with respect to different compilers, compilation options and even across operating systems. We identify robust characteristics and apply standard machine learning algorithms based on these characteristics to automatically identify already known procedures in new malware. This prevents analysts from repeatedly reversing known procedures.

Platform-Independent Recognition of Procedures in Binaries Based on Simple Characteristics