Difference between pages "Email Headers" and "Bulk extractor"

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'''Email Headers''' are lines of [[metadata]] attached to each [[email]] that contain lots of useful information for a [[forensic investigator]]. However, email headers can be easily forged, so they should never be used as the only source of information.
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== Overview ==
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'''bulk_extractor''' is a computer forensics tool that scans a disk image, a file, or a directory of files and extracts useful information without parsing the file system or file system structures. The results can be easily inspected, parsed, or processed with automated tools. '''bulk_extractor''' also created a histograms of features that it finds, as features that are more common tend to be more important. The program can be used for law enforcement, defense, intelligence, and cyber-investigation applications.
  
== Making Sense of Headers ==
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bulk_extractor is distinguished from other forensic tools by its speed and thoroughness. Because it ignores file system structure, bulk_extractor can process different parts of the disk in parallel. In practice, the program splits the disk up into 16MiByte pages and processes one page on each available core. This means that 24-core machines process a disk roughly 24 times faster than a 1-core machine. bulk_extractor is also thorough. That’s because bulk_extractor automatically detects, decompresses, and recursively re-processes compressed data that is compressed with a variety of algorithms. Our testing has shown that there is a significant amount of compressed data in the unallocated regions of file systems that is missed by most forensic tools that are commonly in use today.
  
There is no single way to make sense of email headers. Some examiners favor reading from the bottom up, some favor reading from the top down. Because information in the headers can be put there by the user's [[Mail User Agent|MUA]], a server in transit, or the recipient's [[Mail User Agent|MUA]], it can be difficult to determine when a line was added.
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Another advantage of ignoring file systems is that bulk_extractor can be used to process any digital media. We have used the program to process hard drives, SSDs, optical media, camera cards, cell phones, network packet dumps, and other kinds of digital information.
  
=== Mail User Agents ===
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==Output Feature Files==
{{main|List of MUA Header Formats}}
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Every [[Mail User Agent|MUA]] sets up the headers for a message slightly differently. Although some headers are required under the applicable [http://www.faqs.org/rfcs/rfc2822.html RFC], their format and ordering can vary by client. Almost all clients, however, add their headers in a fixed format and order.
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The examiner can use the format and order for each client to show that messages were forged, but not that they were legitimate. For example, if a message purports to be from [[Apple Mail]] but the order or the headers do not match the [[Apple Mail Header Format]], the message has been forged. If the headers of the message do match that format, however, it does not guarantee that the message was sent by that program.
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=== Servers in Transit ===
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bulk_extractor now creates an output directory that includes:
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* '''ccn.txt''' -- Credit card numbers
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* '''ccn_track2.txt''' -- Credit card “track 2″ information
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* '''domain.txt''' -- Internet domains found on the drive, including dotted-quad addresses found in text.
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* '''email.txt''' -- Email addresses
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* '''ether.txt''' -- Ethernet MAC addresses found through IP packet carving of swap files and compressed system hibernation files and file fragments.
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* '''exif.txt''' -- EXIFs from JPEGs and video segments. This feature file contains all of the EXIF fields, expanded as XML records.
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* '''find.txt''' -- The results of specific regular expression search requests.
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* '''ip.txt''' -- IP addresses found through IP packet carving.
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* '''telephone.txt''' --- US and international telephone numbers.
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* '''url.txt''' --- URLs, typically found in browser caches, email messages, and pre-compiled into executables.
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* '''url_searches.txt''' --- A histogram of terms used in Internet searches from services such as Google, Bing, Yahoo, and others.
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* '''wordlist.txt''' --- :A list of all “words” extracted from the disk, useful for password cracking.
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* '''wordlist_*.txt''' --- The wordlist with duplicates removed, formatted in a form that can be easily imported into a popular password-cracking program.
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* '''zip.txt''' --- A file containing information regarding every ZIP file component found on the media. This is exceptionally useful as ZIP files contain internal structure and ZIP is increasingly the compound file format of choice for a variety of products such as Microsoft Office
  
Mail servers can add lines onto email headers, usually in the form of "Received" lines, like this:
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For each of the above, two additional files may be created:
<pre>Received: by servername.recipeienthost.com (Postfix, from userid 506)
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* '''*_stopped.txt''' --- bulk_extractor supports a stop list, or a list of items that do not need to be brought to the user’s attention. However rather than simply suppressing this information, which might cause something critical to be hidden, stopped entries are stored in the stopped files.
id 77C30808A; Sat, 24 Feb 2007 20:43:56 -0500 (EST)</pre>
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* '''*_histogram.txt''' --- bulk_extractor can also create histograms of features. This is important, as experience has shown that email addresses, domain names, URLs, and other information that appear more frequently on a hard drive or in a cell phone’s memory can be used to rapidly create a pattern of life report.
  
== Message Id Field ==
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Bulk extractor also creates a file that captures the provenance of the run:
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;report.xml
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:A Digital Forensics XML report that includes information about the source media, how the bulk_extractor program was compiled and run, the time to process the digital evidence, and a meta report of the information that was found.
  
According to the current guidelines for email [http://www.faqs.org/rfcs/rfc2822.html RFC 2822]), every email should have a Message-ID field:
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==Post-Processing==
<pre>  The "Message-ID:" field provides a unique message identifier that
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  refers to a particular version of a particular message.  The
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  uniqueness of the message identifier is guaranteed by the host that
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  generates it (see below).  This message identifier is intended to be
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  machine readable and not necessarily meaningful to humans.  A message
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  identifier pertains to exactly one instantiation of a particular
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  message; subsequent revisions to the message each receive new message
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  identifiers.
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  ...
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We have developed four programs for post-processing the bulk_extractor output:
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;bulk_diff.py
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:This program reports the differences between two bulk_extractor runs. The intent is to image a computer, run bulk_extractor on a disk image, let the computer run for a period of time, re-image the computer, run bulk_extractor on the second image, and then report the differences. This can be used to infer the user’s activities within a time period.
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;cda_tool.py
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:This tool, currently under development, reads multiple bulk_extractor reports from multiple runs against multiple drives and performs a multi-drive correlation using Garfinkel’s Cross Drive Analysis technique. This can be used to automatically identify new social networks or to identify new members of existing networks.
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;identify_filenames.py
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:In the bulk_extractor feature file, each feature is annotated with the byte offset from the beginning of the image in which it was found. The program takes as input a bulk_extractor feature file and a DFXML file containing the locations of each file on the drive (produced with Garfinkel’s fiwalk program) and produces an annotated feature file that contains the offset, feature, and the file in which the feature was found.
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;make_context_stop_list.py
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:Although forensic analysts frequently make “stop lists”—for example, a lsit of email addresses that appear in the operating system and should therefore be ignored—such lists have a significant problem. Because it is relatively easy to get an email address into the binary of an open source application, ignoring all of these email addresses may make it possible to cloak email addresses from forensic analysis. Our solution is to create context-sensitive stop lists, in which the feature to be stopped is presented with the context in which it occures. The make_context_stop_list.py program takes the results of multiple bulk_extractor runs and creates a single context-sensitive stop list that can then be used to suppress features when found in a specific context. One such stop list constructed from Windows and Linux operating systems is available on the bulk extractor website.
  
  The message identifier (msg-id) itself MUST be a globally unique
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== Download ==
  identifier for a message.  The generator of the message identifier
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The current version of '''bulk_extractor''' is 1.4.4.  
  MUST guarantee that the msg-id is unique. There are several
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  algorithms that can be used to accomplish this. Since the msg-id has
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  a similar syntax to angle-addr (identical except that comments and
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  folding white space are not allowed), a good method is to put the
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  domain name (or a domain literal IP address) of the host on which the
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  message identifier was created on the right hand side of the "@", and
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  put a combination of the current absolute date and time along with
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  some other currently unique (perhaps sequential) identifier available
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  on the system (for example, a process id number) on the left hand
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  side.  Using a date on the left hand side and a domain name or domain
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  literal on the right hand side makes it possible to guarantee
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  uniqueness since no two hosts use the same domain name or IP address
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  at the same time.  Though other algorithms will work, it is
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  RECOMMENDED that the right hand side contain some domain identifier
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  (either of the host itself or otherwise) such that the generator of
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  the message identifier can guarantee the uniqueness of the left hand
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  side within the scope of that domain.</pre>
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Where known, the Message-ID algorithms for known programs are [[List of MUA Header Formats|given on the separate pages for those programs]].
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* Downloads are available at: http://digitalcorpora.org/downloads/bulk_extractor/
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* A WIndows installer with the GUI can be downloaded from: http://www.digitalcorpora.org/downloads/bulk_extractor/bulk_extractor-1.4.1-windowsinstaller.exe
  
== Sample Header ==  
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== Bibliography ==
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=== Academic Publications ===
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# Garfinkel, Simson, [http://simson.net/clips/academic/2013.COSE.bulk_extractor.pdf Digital media triage with bulk data analysis and bulk_extractor]. Computers and Security 32: 56-72 (2013)
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# Beverly, Robert, Simson Garfinkel and Greg Cardwell, [http://simson.net/clips/academic/2011.DFRWS.ipcarving.pdf "Forensic Carving of Network Packets and Associated Data Structures"], DFRWS 2011, Aug. 1-3, 2011, New Orleans, LA. BEST PAPER AWARD (Acceptance rate: 23%, 14/62)
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#Garfinkel, S., [http://simson.net/clips/academic/2006.DFRWS.pdf Forensic Feature Extraction and Cross-Drive Analysis,]The 6th Annual Digital Forensic Research Workshop Lafayette, Indiana, August 14-16, 2006. (Acceptance rate: 43%, 16/37)
  
This is an (incomplete) excerpt from an email header:
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===YouTube===
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'''[http://www.youtube.com/results?search_query=bulk_extractor search YouTube] for bulk_extractor videos'''
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* [http://www.youtube.com/watch?v=odvDTGA7rYI Simson Garfinkel speaking at CERIAS about bulk_extractor]
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* [http://www.youtube.com/watch?v=wTBHM9DeLq4 BackTrack 5 with bulk_extractor]
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* [http://www.youtube.com/watch?v=QVfYOvhrugg Ubuntu 12.04 forensics with bulk_extractor]
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* [http://www.youtube.com/watch?v=57RWdYhNvq8 Social Network forensics with bulk_extractor]
  
Received: from lists.securityfocus.com (lists.securityfocus.com [205.206.231.19])
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===Tutorials===
        by outgoing2.securityfocus.com (Postfix) with QMQP
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# [http://simson.net/ref/2012/2012-08-08%20bulk_extractor%20Tutorial.pdf Using bulk_extractor for digital forensics triage and cross-drive analysis], DFRWS 2012
        id 7E9971460C9; Mon,  9 Jan 2006 08:01:36 -0700 (MST)
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Mailing-List: contact forensics-help@securityfocus.com; run by ezmlm
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Precedence: bulk
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List-Id: <forensics.list-id.securityfocus.com>
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List-Post: <mailto:forensics@securityfocus.com>
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List-Help: <mailto:forensics-help@securityfocus.com>
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List-Unsubscribe: <mailto:forensics-unsubscribe@securityfocus.com>
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List-Subscribe: <mailto:forensics-subscribe@securityfocus.com>
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Delivered-To: mailing list forensics@securityfocus.com
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Delivered-To: moderator for forensics@securityfocus.com
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Received: (qmail 20564 invoked from network); 5 Jan 2006 16:11:57 -0000
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From: YJesus <yjesus@security-projects.com>
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To: forensics@securityfocus.com
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Subject: New Tool : Unhide
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User-Agent: KMail/1.9
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MIME-Version: 1.0
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Content-Disposition: inline
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Date: Thu, 5 Jan 2006 16:41:30 +0100
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Content-Type: text/plain;
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  charset="iso-8859-1"
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Content-Transfer-Encoding: quoted-printable
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Message-Id: <200601051641.31830.yjesus@security-projects.com>
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X-HE-Spam-Level: /
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X-HE-Spam-Score: 0.0
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X-HE-Virus-Scanned: yes
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Status: RO
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Content-Length: 586
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Lines: 26
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== External Links ==
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* http://en.wikipedia.org/wiki/Computer_forensics#E-mail_Headers
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* http://www.forensictracer.com software for forensic analysis of internet resources
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Latest revision as of 15:24, 19 May 2014

Overview

bulk_extractor is a computer forensics tool that scans a disk image, a file, or a directory of files and extracts useful information without parsing the file system or file system structures. The results can be easily inspected, parsed, or processed with automated tools. bulk_extractor also created a histograms of features that it finds, as features that are more common tend to be more important. The program can be used for law enforcement, defense, intelligence, and cyber-investigation applications.

bulk_extractor is distinguished from other forensic tools by its speed and thoroughness. Because it ignores file system structure, bulk_extractor can process different parts of the disk in parallel. In practice, the program splits the disk up into 16MiByte pages and processes one page on each available core. This means that 24-core machines process a disk roughly 24 times faster than a 1-core machine. bulk_extractor is also thorough. That’s because bulk_extractor automatically detects, decompresses, and recursively re-processes compressed data that is compressed with a variety of algorithms. Our testing has shown that there is a significant amount of compressed data in the unallocated regions of file systems that is missed by most forensic tools that are commonly in use today.

Another advantage of ignoring file systems is that bulk_extractor can be used to process any digital media. We have used the program to process hard drives, SSDs, optical media, camera cards, cell phones, network packet dumps, and other kinds of digital information.

Output Feature Files

bulk_extractor now creates an output directory that includes:

  • ccn.txt -- Credit card numbers
  • ccn_track2.txt -- Credit card “track 2″ information
  • domain.txt -- Internet domains found on the drive, including dotted-quad addresses found in text.
  • email.txt -- Email addresses
  • ether.txt -- Ethernet MAC addresses found through IP packet carving of swap files and compressed system hibernation files and file fragments.
  • exif.txt -- EXIFs from JPEGs and video segments. This feature file contains all of the EXIF fields, expanded as XML records.
  • find.txt -- The results of specific regular expression search requests.
  • ip.txt -- IP addresses found through IP packet carving.
  • telephone.txt --- US and international telephone numbers.
  • url.txt --- URLs, typically found in browser caches, email messages, and pre-compiled into executables.
  • url_searches.txt --- A histogram of terms used in Internet searches from services such as Google, Bing, Yahoo, and others.
  • wordlist.txt --- :A list of all “words” extracted from the disk, useful for password cracking.
  • wordlist_*.txt --- The wordlist with duplicates removed, formatted in a form that can be easily imported into a popular password-cracking program.
  • zip.txt --- A file containing information regarding every ZIP file component found on the media. This is exceptionally useful as ZIP files contain internal structure and ZIP is increasingly the compound file format of choice for a variety of products such as Microsoft Office

For each of the above, two additional files may be created:

  • *_stopped.txt --- bulk_extractor supports a stop list, or a list of items that do not need to be brought to the user’s attention. However rather than simply suppressing this information, which might cause something critical to be hidden, stopped entries are stored in the stopped files.
  • *_histogram.txt --- bulk_extractor can also create histograms of features. This is important, as experience has shown that email addresses, domain names, URLs, and other information that appear more frequently on a hard drive or in a cell phone’s memory can be used to rapidly create a pattern of life report.

Bulk extractor also creates a file that captures the provenance of the run:

report.xml
A Digital Forensics XML report that includes information about the source media, how the bulk_extractor program was compiled and run, the time to process the digital evidence, and a meta report of the information that was found.

Post-Processing

We have developed four programs for post-processing the bulk_extractor output:

bulk_diff.py
This program reports the differences between two bulk_extractor runs. The intent is to image a computer, run bulk_extractor on a disk image, let the computer run for a period of time, re-image the computer, run bulk_extractor on the second image, and then report the differences. This can be used to infer the user’s activities within a time period.
cda_tool.py
This tool, currently under development, reads multiple bulk_extractor reports from multiple runs against multiple drives and performs a multi-drive correlation using Garfinkel’s Cross Drive Analysis technique. This can be used to automatically identify new social networks or to identify new members of existing networks.
identify_filenames.py
In the bulk_extractor feature file, each feature is annotated with the byte offset from the beginning of the image in which it was found. The program takes as input a bulk_extractor feature file and a DFXML file containing the locations of each file on the drive (produced with Garfinkel’s fiwalk program) and produces an annotated feature file that contains the offset, feature, and the file in which the feature was found.
make_context_stop_list.py
Although forensic analysts frequently make “stop lists”—for example, a lsit of email addresses that appear in the operating system and should therefore be ignored—such lists have a significant problem. Because it is relatively easy to get an email address into the binary of an open source application, ignoring all of these email addresses may make it possible to cloak email addresses from forensic analysis. Our solution is to create context-sensitive stop lists, in which the feature to be stopped is presented with the context in which it occures. The make_context_stop_list.py program takes the results of multiple bulk_extractor runs and creates a single context-sensitive stop list that can then be used to suppress features when found in a specific context. One such stop list constructed from Windows and Linux operating systems is available on the bulk extractor website.

Download

The current version of bulk_extractor is 1.4.4.

Bibliography

Academic Publications

  1. Garfinkel, Simson, Digital media triage with bulk data analysis and bulk_extractor. Computers and Security 32: 56-72 (2013)
  2. Beverly, Robert, Simson Garfinkel and Greg Cardwell, "Forensic Carving of Network Packets and Associated Data Structures", DFRWS 2011, Aug. 1-3, 2011, New Orleans, LA. BEST PAPER AWARD (Acceptance rate: 23%, 14/62)
  3. Garfinkel, S., Forensic Feature Extraction and Cross-Drive Analysis,The 6th Annual Digital Forensic Research Workshop Lafayette, Indiana, August 14-16, 2006. (Acceptance rate: 43%, 16/37)

YouTube

search YouTube for bulk_extractor videos

Tutorials

  1. Using bulk_extractor for digital forensics triage and cross-drive analysis, DFRWS 2012