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Data fragment forensics for embedded DVR systems
Affiliation:1. Institut de Recherche Criminelle de la Gendarmerie Nationale (IRCGN), Digital Forensics department (INL), 1 boulevard Théophile Sueur, 93110 Rosny-Sous-Bois, France;2. PRES Sorbonne Universités – Université Panthéon-Assas Paris II, 12 place de Panthéon, 75005 Paris Cedex 05, France;3. Kriminaltechnisches Institut (KTI) des Bundeskriminalamtes (BKA), Äppelallee 45, 65173 Wiesbaden, Germany;1. Institut de Recherche Criminelle de la Gendarmerie Nationale (IRCGN), Digital Forensics Department (INL), 1 boulevard Théophile Sueur, 93110, Rosny-Sous-Bois, France;2. PRES Sorbonne Universités – Université Panthéon-Assas Paris II, 12 place de Panthéon, 75005, Paris Cedex 05, France;3. ENSICAEN, 6 boulevard maréchal Juin, 14050, Caen Cedex 4, France;4. INSA Lyon, 20 avenue Albert Einstein, 69100, Villeurbanne, France;1. dence GmbH, Dresden, Germany;2. University of Münster, Department of Information Systems, Münster, Germany
Abstract:A recent increase in the prevalence of embedded systems has led them to become a primary target of digital forensic investigations. Embedded systems with DVR (Digital Video Recorder) capabilities are able to generate multimedia (video/audio) data, and can act as vital pieces of evidence in the field of digital forensics.To counter anti-forensics, it is necessary to derive systematic forensic techniques that can be used on data fragments in unused (unallocated) areas of files or images. Specifically, the techniques should extract meaningful information from various types of data fragments, such as non-sequential fragmentation and missing fragments overwritten by other data.This paper proposes a new digital forensic system for use on video data fragments related to DVRs. We demonstrate in detail special techniques for the classification, reassembly, and extraction of video data fragments, and introduce an integrated framework for data fragment forensics based on techniques described in this paper.
Keywords:Digital forensics  Data fragment forensics  Data fragmentation  DVR  CCTV  Vehicle black box  Unused area
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