Over 20 studies have been published in the past decade involving file and data type classification for digital forensics and information security applications. Methods using n-grams as inputs have proven the most successful across a wide variety of types; however, there are mixed results regarding the utility of unigrams and bigrams as inputs independently. In this study, we use support vector machines (SVMs) consisting of unigrams and bigrams, as well as complexity and other byte

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... d measures, as inputs. Using concatenated unigrams and bigrams as input and a linear kernel SVM, we achieve significantly improved results over those previously reported (73.4% classification rate across 38 file and data types). We are the first to use concatenated n-grams as the sole input, and we show their superiority over inputs used previously. We also found that too many different types of features as inputs result in overfitting and poor generalization properties. We include several types seldom or not studied in the past (Microsoft Office 2010 files, file system data, base64, base85, URL encoding, flash video, M4A, MP4, WMV, and JSON records). The "winning" approach is instantiated in an open source software tool called Sceadan-Systematic Classification Engine for Advanced Data ANalysis.