Difference between revisions of "Automated Computer Forensics"

From Simson Garfinkel
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===Bulk Data Forensics===
 
===Bulk Data Forensics===
Bulk Data Forensics is forensics that is based on a bulk analysis of disk images or other kinds of forensic source data. Bulk data forensics has several important advantages over traditional forensic processing:
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Bulk Data Forensics is based on the bulk analysis of disk images or other kinds of forensic source data. Bulk data forensics has several important advantages over traditional forensic processing:
 
* It's faster, because the disk head scans the disk (or disk image)  from beginning to end without having to seek from file to file.
 
* It's faster, because the disk head scans the disk (or disk image)  from beginning to end without having to seek from file to file.
 
* It can tolerate media that is damaged or incomplete, since the forensic processing does not require the reconstruction of file allocation tables, disk directories, or metadata.
 
* It can tolerate media that is damaged or incomplete, since the forensic processing does not require the reconstruction of file allocation tables, disk directories, or metadata.

Revision as of 18:05, 27 April 2009

We are developing a variety of techniques and tools for performing Automated Document and Media Exploitation (ADOMEX). The thrust of this research consists of several thrusts:

  1. Developing open source tools for working with electronic evidence. This work is part of the AFF project[1].
  2. Developing an unclassified Real Data Corpus (RDC) consisting of "real data from real people" that can be used to develop new algorithms and test automated tools.
  3. Developing new algorithms and approaches for working in a "data-rich environment."

Recent Research Developments

File-based Forensics

File-based forensics is forensics that is based on an analysis of files, deleted files and orphan files. Most forensics currently performed for law enforcement, commercial e-discovery, and for intelligence purposes is based on file forensics. The goal here is typically to find a specific file that can be shown to a jury or that contains actionable intelligence. File forensics is typically performed using programs such as EnCase, FTK, or SleuthKit.

  • We have developed a batch analysis tool called system called fiwalk which can take a disk image and produce an XML file corresponding to all of the files, deleted files, orphan files, and all of the extracted file metadata from a disk image. This XML file can be used as an input to enable further automated media processing. Using this system we have created a variety of applications for reporting and manipulating disk images. We have also developed an efficient system for allowing remote file-level access of disk images using XML-RPC and REST. Details can be found in our paper[2].
  • We have developed a prototype system for performing automated media forensic reporting. Based on PyFlag, the system performs an in-depth analysis of captured media, locates local and online identities, and presents summary information in a report that is tailed to be easy for the consumer of forensic intelligence[3].

Bulk Data Forensics

Bulk Data Forensics is based on the bulk analysis of disk images or other kinds of forensic source data. Bulk data forensics has several important advantages over traditional forensic processing:

  • It's faster, because the disk head scans the disk (or disk image) from beginning to end without having to seek from file to file.
  • It can tolerate media that is damaged or incomplete, since the forensic processing does not require the reconstruction of file allocation tables, disk directories, or metadata.
  • It works with obscure or unknown operating systems, since no attempt is made to reconstruct the file system or other operating system structures.
  • It lends itself to statistical processing. Instead of scanning the entire disk image, the image can be sampled.

We have developed several interesting tools for bulk data forensics:

  • frag_find is a tool that can report if sectors of a TARGET file are present on a disk image. This is useful in cases where a TARGET file has been stolen and you wish to establish that the file has been present on a subject's drive. If most of the TARGET file's sectors are found on the IMAGE drive---and if the sectors are in consecutive sector runs---then the chances are excellent that the file was once there. Frag_find performs this search using time-and-space efficient data structures arranged in multiple filtering layers. The program deals with the problem of non-unique blocks by looking for runs of matching blocks, rather than individual blocks. Frag_find is part of the NPS Bloom package, which can be downloaded from http://www.afflib.org.
  • bulk_extractor is a tool that searches for recognized features in the bulk data and performs histogram analysis on the result. You can write your own feature extractors using flex. We provide bulk_extractor with an extractor that finds complete email addresses and domain names. The most common email address on a hard drive is usually that of the drive's primary use; other top-occuring email addresses tend to belong to that person's primary correspondents. By looking at the list of email addresses sorted by frequency, it is easy to rapidly infer the user's social network.
  • CDA Tool takes the results of the bulk_extractor tool and performs a Cross-Drive Analysis. This can allow an investigator to discover previously unknown social networks in a set of hard drives, or to see if a newly acquired hard drive belongs to an existing social network.

Relevant Publications

  1. "AFF: A New Format for Storing Hard Drive Images," Garfinkel, S., Communications of the ACM, February, 2006
  2. Automating Disk Forensic Processing with SleuthKit, XML and Python, Fourth International IEEE Workshop on Systematic Approaches to Digital Forensic Engineering (IEEE/SADFE'09), May 2009
  3. A Framework for Automated Digital Forensic Reporting, Lt. Paul Farrell, Master's Thesis, Naval Postgraduate School, Monterey, CA, March 2009

See also: