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We present a second iteration of a machine learning approach to static codeanalysis and fingerprinting for weaknesses related to security, softwareengineering, and others using the open-source MARF framework and the MARFCATapplication based on it for the NIST's SATE IV static analysis tool expositionworkshop's data sets that include additional test cases, including new largesynthetic cases. To aid detection of weak or vulnerable code, including sourceor binary on different platforms the machine learning approach proved to befast and accurate to for such tasks where other tools are either much slower orhave much smaller recall of known vulnerabilities. We use signal and NLPprocessing techniques in our approach to accomplish the identification andclassification tasks. MARFCAT's design from the beginning in 2010 made isindependent of the language being analyzed, source code, bytecode, or binary.In this follow up work with explore some preliminary results in this area. Weevaluated also additional algorithms that were used to process the data.

MARFCAT: Transitioning to Binary and Larger Data Sets of SATE IV