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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Fast Content-Based File Type Identification
verfasst von : Irfan Ahmed, Kyung-Suk Lhee, Hyun-Jung Shin, Man-Pyo Hong
Erschienen in: Advances in Digital Forensics VII
Verlag: Springer Berlin Heidelberg
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Digital forensic examiners often need to identify the type of a file or file fragment based on the content of the file. Content-based file type identification schemes typically use a byte frequency distribution with statistical machine learning to classify file types. Most algorithms analyze the entire file content to obtain the byte frequency distribution, a technique that is inefficient and time consuming. This paper proposes two techniques for reducing the classification time. The first technique selects a subset of features based on the frequency of occurrence. The second speeds up classification by randomly sampling file blocks. Experimental results demonstrate that up to a fifteen-fold reduction in computational time can be achieved with limited impact on accuracy.