File Detection On Network Traffic Using Approximate Matching

<p>In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequenc...

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Main Authors: Frank Breitinger, Ibrahim Baggili
Format: Article
Language:English
Published: Association of Digital Forensics, Security and Law 2014-09-01
Series:Journal of Digital Forensics, Security and Law
Online Access:http://ojs.jdfsl.org/index.php/jdfsl/article/view/261
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spelling doaj-1e57138e637f408e9410bb4a8aaa1ba82020-11-25T01:49:25ZengAssociation of Digital Forensics, Security and LawJournal of Digital Forensics, Security and Law1558-72151558-72232014-09-01922336167File Detection On Network Traffic Using Approximate MatchingFrank Breitinger0Ibrahim Baggili1University of New HavenUniversity of New Haven<p>In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS) have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial.</p><p>Within this paper we present a new technique for network trac analysis based on approximate matching (a.k.a fuzzy hashing) which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive conguration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10<sup>-4</sup> and 10<sup>-5</sup> and an algorithm throughput of over 650 Mbit/s.</p>http://ojs.jdfsl.org/index.php/jdfsl/article/view/261
collection DOAJ
language English
format Article
sources DOAJ
author Frank Breitinger
Ibrahim Baggili
spellingShingle Frank Breitinger
Ibrahim Baggili
File Detection On Network Traffic Using Approximate Matching
Journal of Digital Forensics, Security and Law
author_facet Frank Breitinger
Ibrahim Baggili
author_sort Frank Breitinger
title File Detection On Network Traffic Using Approximate Matching
title_short File Detection On Network Traffic Using Approximate Matching
title_full File Detection On Network Traffic Using Approximate Matching
title_fullStr File Detection On Network Traffic Using Approximate Matching
title_full_unstemmed File Detection On Network Traffic Using Approximate Matching
title_sort file detection on network traffic using approximate matching
publisher Association of Digital Forensics, Security and Law
series Journal of Digital Forensics, Security and Law
issn 1558-7215
1558-7223
publishDate 2014-09-01
description <p>In recent years, Internet technologies changed enormously and allow faster Internet connections, higher data rates and mobile usage. Hence, it is possible to send huge amounts of data / files easily which is often used by insiders or attackers to steal intellectual property. As a consequence, data leakage prevention systems (DLPS) have been developed which analyze network traffic and alert in case of a data leak. Although the overall concepts of the detection techniques are known, the systems are mostly closed and commercial.</p><p>Within this paper we present a new technique for network trac analysis based on approximate matching (a.k.a fuzzy hashing) which is very common in digital forensics to correlate similar files. This paper demonstrates how to optimize and apply them on single network packets. Our contribution is a straightforward concept which does not need a comprehensive conguration: hash the file and store the digest in the database. Within our experiments we obtained false positive rates between 10<sup>-4</sup> and 10<sup>-5</sup> and an algorithm throughput of over 650 Mbit/s.</p>
url http://ojs.jdfsl.org/index.php/jdfsl/article/view/261
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