Researches up to data have focused on using non texture based methods in addressing the problem of classifying the data types of file fragments. In this research we considered a file fragment as a 8 bit grayscale image and the Gray Level Co-Occurrence Matrix (GLCM) based method was used to extract textural features. Texture features for fragment dimensions 8 × 8, 16 × 16, 32 × 32 and 64 × 64 and gray level quantizations from 4 to 64 with step increments of 4 were explored. The K nearest neighbor classifier was used as the classifier and the optimal GLCM features for a particular gray level and fragment dimension were determined using Sequential Forward Selection (SFS) algorithm. On the classification of 7 data types, our novel approach reached a maximum overall accuracy of 86.86% in classifying 64 × 64 sized fragments with 12 gray levels.
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