Difference between pages "File Carving Bibliography" and "GRR"

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; [http://citeseer.ist.psu.edu/shanmugasundaram03automatic.html  Automatic Reassembly of Document Fragments via Context Based Statistical Models], Kulesh Shanmugasundaram and Nasir Memon.
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{{Infobox_Software |
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  name = Rekall |
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  maintainer = [[Darren Bilby]] and others |
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  os = {{Cross-platform}} |
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  genre = {{Incident response}} |
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  license = {{APL}} |
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  website = [https://code.google.com/p/grr/ code.google.com/p/grr/] |
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}}
  
<bibtex>
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GRR is an Incident Response Framework focused on Remote Live Forensics.
@article{
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  journal="Journal of Digital Forensic Practice", 
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  publisher="Taylor & Francis",
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  author="Yoginder Singh Dandass and Nathan Joseph Necaise and Sherry Reede Thomas",
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  title="An Empirical Analysis of Disk Sector Hashes for Data Carving",
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  year=2008,
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  volume=2,
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  issue=2,
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  pages="95--106",
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  abstract="Discovering known illicit material on digital storage devices is an important component of a digital forensic investigation. Using existing data carving techniques and tools, it is typically difficult to recover remaining fragments of deleted illicit files whose file system metadata and file headers have been overwritten by newer files. In such cases, a sector-based scan can be used to locate those sectors whose content matches those of sectors from known illicit files. However, brute-force sector-by-sector comparison is prohibitive in terms of time required. Techniques that compute and compare hash-based signatures of sectors in order to filter out those sectors that do not produce the same signatures as sectors from known illicit files are required for accelerating the process.
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This article reports the results of a case study in which the hashes for over 528 million sectors extracted from over 433,000 files of different types were analyzed. The hashes were computed using SHA1, MD5, CRC64, and CRC32 algorithms and hash collisions of sectors from JPEG and WAV files to other sectors were recorded. The analysis of the results shows that although MD5 and SHA1 produce no false-positive indications, the occurrence of false positives is relatively low for CRC32 and especially CRC64. Furthermore, the CRC-based algorithms produce considerably smaller hashes than SHA1 and MD5, thereby requiring smaller storage capacities. CRC64 provides a good compromise between number of collisions and storage capacity required for practical implementations of sector-scanning forensic tools.",
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= See also =
  url="http://www.informaworld.com/10.1080/15567280802050436"
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* [[rekall]]
}
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</bibtex>
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[[Category:Bibliographies]]
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= External Links =
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* [https://code.google.com/p/grr/ Project site]
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* [https://code.google.com/p/grr/wiki/ProjectFAQ Project FAQ]
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* [http://grr.googlecode.com/git/docs/index.html Documentation]
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== Publications ==
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* [http://static.googleusercontent.com/media/research.google.com/en/us/pubs/archive/37237.pdf Distributed forensics and incident response in the enterprise], by [[Michael Cohen]], [[Darren Bilby]], G. Caronni. Digital Investigation, 2011.
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* [https://googledrive.com/host/0B9hc84IflFGbN2IwMTUyYTUtMTU0Mi00ZWQ3LWFhNDktM2IyMTg5MmY3OWI0/Hunting%20in%20the%20Enterprise:%20Forensic%20Triage%20and%20Incident%20Response Hunting in the enterprise: Forensic triage and incident response], by [[Andreas Moser], [[Michael Cohen]], Digital Investigation, 2013.
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== Presentations ==
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* [https://googledrive.com/host/0B1wsLqFoT7i2N3hveC1lSEpHUnM/Docs/GRR%20Rapid%20Response%20-%20OSFC%202012.pdf OSDFC 2012 GRR Overview], by [[Darren Bilby]]
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== Workshops ==
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* [https://drive.google.com/?usp=chrome_app#folders/0B1wsLqFoT7i2eU1jU0JldW9JUU0 OSDFC workshop 2013] , by [[Darren Bilby]]

Revision as of 14:20, 12 January 2014

Rekall
Maintainer: Darren Bilby and others
OS: Cross-platform
Genre: Incident Response
License: APL
Website: code.google.com/p/grr/

GRR is an Incident Response Framework focused on Remote Live Forensics.

Contents

See also

External Links

Publications

Presentations

Workshops