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Fast Content-Based File Type Identification

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Názov: Fast Content-Based File Type Identification
Autori: Ahmed, Irfan, Lhee, Kyung-Suk, Shin, Hyun-Jung, Hong, Man-Pyo
Prispievatelia: Information Security Institute, Queensland University of Technology Brisbane (QUT), Ajou University, Gilbert Peterson, Sujeet Shenoi, TC 11, WG 11.9
Zdroj: IFIP Advances in Information and Communication Technology ; 7th Digital Forensics (DF) ; https://inria.hal.science/hal-01569553 ; 7th Digital Forensics (DF), Jan 2011, Orlando, FL, United States. pp.65-75, ⟨10.1007/978-3-642-24212-0_5⟩
Informácie o vydavateľovi: CCSD
Springer
Rok vydania: 2011
Predmety: File type identification, file content classification, byte frequency, [INFO]Computer Science [cs]
Geografické téma: Orlando, FL, United States
Popis: Part 2: FORENSIC TECHNIQUES ; International audience ; 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.
Druh dokumentu: conference object
Jazyk: English
DOI: 10.1007/978-3-642-24212-0_5
Dostupnosť: https://inria.hal.science/hal-01569553
https://inria.hal.science/hal-01569553v1/document
https://inria.hal.science/hal-01569553v1/file/978-3-642-24212-0_5_Chapter.pdf
https://doi.org/10.1007/978-3-642-24212-0_5
Rights: http://creativecommons.org/licenses/by/ ; info:eu-repo/semantics/OpenAccess
Prístupové číslo: edsbas.54718D05
Databáza: BASE
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  Data: <searchLink fieldCode="AR" term="%22Ahmed%2C+Irfan%22">Ahmed, Irfan</searchLink><br /><searchLink fieldCode="AR" term="%22Lhee%2C+Kyung-Suk%22">Lhee, Kyung-Suk</searchLink><br /><searchLink fieldCode="AR" term="%22Shin%2C+Hyun-Jung%22">Shin, Hyun-Jung</searchLink><br /><searchLink fieldCode="AR" term="%22Hong%2C+Man-Pyo%22">Hong, Man-Pyo</searchLink>
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  Data: Information Security Institute<br />Queensland University of Technology Brisbane (QUT)<br />Ajou University<br />Gilbert Peterson<br />Sujeet Shenoi<br />TC 11<br />WG 11.9
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  Data: IFIP Advances in Information and Communication Technology ; 7th Digital Forensics (DF) ; https://inria.hal.science/hal-01569553 ; 7th Digital Forensics (DF), Jan 2011, Orlando, FL, United States. pp.65-75, ⟨10.1007/978-3-642-24212-0_5⟩
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  Data: Part 2: FORENSIC TECHNIQUES ; International audience ; 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.
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