A Novel Support Vector Machine Approach to High Entropy Data Fragment Classification.
Qiming Li, Alvin Y. Ong, Ponnuthurai Nagaratnam Suganthan, Vrizlynn L. L. Thing · 2010
A major challenge in digital forensics is the efficient and accurate file type classification of a fragment of evidence data, in the absence of header and file system information. A typical approach to this problem is to classify the fragment based on simple statistics, such as the entropy and the statistical distance of byte histograms. This approach is ineffective when dealing with high entropy data, such as multimedia and compressed files, all of which often appear to be random. We propose a method incorporating a support vector machine (SVM). In particular, we extract feature vectors from the byte frequencies of a given fragment, and use an SVM to predict the type of the fragment under supervised learning. Our method is efficient and achieves high accuracy for high entropy data fragments.