Fast file-type identification

Authors:
Irfan Ahmed;Kyung-suk Lhee;Hyunjung Shin;ManPyo Hong
Affiliations:
Ajou University, South Korea;Ajou University, South Korea;Ajou University, South Korea;Ajou University, South Korea
Venue:
Proceedings of the 2010 ACM Symposium on Applied Computing
Year:
2010

Citing 1
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Abstract

This paper proposes two techniques to reduce the classification time of content-based file type identification. The first is a feature selection technique, which uses a subset of highly-occurring byte patterns in building the representative model of a file type and classifying files. The second is a content sampling technique, which uses a subset of file content in obtaining its byte-frequency distribution. Our initial experiments show that the proposed approaches are promising even the simple 1-gram features are used for the classification.