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Advancements in Digital Forensics Research

Explore the latest digital forensics tools and techniques for recovering, preserving, and examining digital evidence. Learn about the importance of digital forensics and the challenges faced in the field.

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Advancements in Digital Forensics Research

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  1. Research in Next-Generation Digital Forensics Golden G. Richard III, Ph.D. Associate Professor Dept. of Computer Science golden@cs.uno.edu http://www.cs.uno.edu/~golden

  2. Digital Forensics Research Group • Fall 2006: • Thursdays @ 1pm in NSSAL (Math 322) • Primary Collaborators: • Vassil Roussev [UNO CS] • Vico Marziale [UNO Ph.D. student] • Frank Adelstein [ATC-NY]

  3. Digital Forensics Definition: “Tools and techniques to recover, preserve, and examine digital evidence on or transmitted by digital devices.” Devices include computers, PDAs, cellular phones, videogame consoles, copy machines, printers, …

  4. Examples of Digital Evidence • Threatening emails • Documents (e.g., in places they shouldn’t be) • Suicide notes • Bomb-making diagrams • Malicious Software • Viruses • Worms • … • Child pornography (contraband) • Evidence that network connections were made between machines • Cell phone SMS messages

  5. Facts (or: Why Digital Forensics?) • Deleted files aren’t securely deleted • Recover deleted file + when it was deleted! • Renaming files to avoid detection is pointless • Formatting disks doesn’t delete much data • Web-based email can be (partially) recovered directly from a computer • Files transferred over a network can be reassembled and used as evidence

  6. Facts (2) • Uninstalling applications is much more difficult than it might appear… • “Volatile” data hangs around for a long time (even across reboots) • Remnants from previously executed applications • Using encryption properly is difficult, because data isn’t useful unless decrypted • Anti-forensics (privacy-enhancing) software is mostly broken • “Big” magnets (generally) don’t work • Media mutilation (except in the extreme) doesn’t work • Basic enabler: Data is very hard to kill

  7. Privacy Through Media Mutilation or or or forensically-secure file deletion software (but make sure it works!) degausser

  8. Digital Forensics Process • Identification of potential digital evidence • Where might the evidence be? • Which devices did the suspect use? • Preservation and copying of evidence • On the crime scene… • First, stabilize evidence…prevent loss and contamination • If possible, make identical copies of evidence for examination • Careful examination of evidence • Presentation • “The FAT was fubared, but using a hex editor I changed the first byte of directory entry 13 from 0xEF to 0x08 to restore ‘HITLIST.DOC’…” • “The suspect attempted to hide the Microsoft Word document ‘HITLIST.DOC’ but I was able to recover it without tampering with the file contents.” • Legal: Balance of need to investigate vs. privacy

  9. “Traditional” Digital Forensics • Pull the plug • “Image” (make bit-perfect copies) of hard drives, floppies, USB keys, etc. • Use forensics software to analyze copies of drives • Investigator typically uses a single computer to perform investigation in the lab • Present results to client, to officer-in-charge, court

  10. Traditional: Where’s the evidence? • Undeleted files, expect some names to be incorrect • Deleted files • Windows registry • Print spool files • Hibernation files • Temp files (all those .TMP files!) • Slack space • Swap files • Browser caches • Alternate or “hidden” partitions • On a variety of removable media (floppies, ZIP, Jazz, tapes, …)

  11. But Evidence is Also… • In RAM • “In” the network • On machine-critical machines • Can’t turn off without severe disruption • Can’t turn them ALL off just to see! • On huge storage devices • 1TB server: image entire machine and drag it back to the lab to see if it’s interesting? • 10TB?

  12. Next Generation: Needs • Broad: • Better design, better software • Yes, some of it is engineering (and hacking) • Someone has to do it • Better vision, application of ‘real’ CS to problems • More specific: • Need for speed • Machine correlation • Machine profiling • Better auditing of investigative process • On-the-spot forensics: Triage • Live forensics • Network forensics • Specific tools for detection and remediation of malware • Phishing investigation • …

  13. Next Generation: UNO • Better file carving • Forensic-aware OS components • In-place file carving • Forensic accountability • On-the-spot forensics • Distributed digital forensics

  14. File Carving: Basic Idea one cluster unrelated disk blocks interesting file one sector header, e.g., 0x474946e8e761 (GIF) footer, e.g., 0x003B (GIF) “milestones” or “anti-milestones”

  15. File Carving: Fragmentation header, e.g., 0x474946e8e761 (GIF) footer, e.g., 0x003B (GIF) “milestones” or “anti-milestones”

  16. File Carving: Fragmentation header, e.g., 0x474946e8e761 (GIF) footer, e.g., 0x003B (GIF)

  17. File Carving: Damaged Files No footer header, e.g., 0x474946e8e761 (GIF) “milestones” or “anti-milestones”

  18. File Carving: Doing a Better Job • Better design • Faster • Distributed implementation • More flexible description of file types • Automatic generation of type descriptions • Patterns • Rule sets • Multiple-pass carving • Carve, “remove” validated files from block list, re-carve, hope that some fragmented files coalesce • Block-sniffing

  19. File Carving: Block Sniffing header, e.g., 0x474946e8e761 (GIF) • Do these blocks “smell” right? • N-gram analysis • entropy tests • parsing

  20. Better Software: File Carving: Scalpel • Two-pass design • Minimizes: • Reads • Seeks • Writes • Data copying • Memory usage • Doesn’t yet incorporate all of the carving wizardry we have in mind G. G. Richard III, V. Roussev, "Scalpel: A Frugal, High Performance File Carver," Proceedings of the 2005 Digital Forensics Research Workshop (DFRWS 2005), New Orleans, LA.

  21. Some Scalpel Results (1) Tread + 238,270,750,000 bytes Big targets, large carve sizes, huge improvement (over 5 hours faster)

  22. Some Scalpel Results (2) Tread + 117,622,357,936 bytes Big targets, large carve sizes, huge improvement (over 7 hours faster)

  23. OS Support for Digital Forensics • Export raw disk devices across network for processing • Others: network block device (NBD) • Us: optimization • “In-place” file carving • Us: Export results from file carving as a filesystem, w/ minimal extra storage • Better auditing of investigative process • Us: “digital evidence bag”-aware filesystems

  24. FUSE (Filesystem in User Space) Filesystem Implementation dd if=/evidence/DEC/img.dd of=copy.dd FUSE library read() user space C library C library kernel space Linux Virtual File System Interface (VFS) FUSE ext3 reiserFS

  25. In-Place File Carving client applications scalpel_fs FUSE preview database nbd client local drive network nbd server remote drive Scalpel G. G. Richard III, V. Roussev, V. Marziale, “In-Place File Carving,” submitted to the Third Annual IFIP WG 11.9 International Conference on Digital Forensics, 2007.

  26. Better Auditing • Want: Digital Evidence Bags • See: P. Turner, “Unification of Digital Evidence from Disparate Sources (Digital Evidence Bags),” DFRWS 2005 • See: Common Digital Evidence Storage Format (CDESF) working group, http://www.dfrws.org/CDESF/.

  27. Better Auditing (2) … dd scalpel TSK FTK Applications (User space) Block-level Data Access Filesystem Data Access (Kernel) Operating System VFS Interface FDAM Digital Evidence Container FDAM Block Device Import/ Export DEC (DEB, AFF, Gfzip …) Audit Log Evidence Data G. G. Richard III, V. Roussev, "Toward Secure, Audited Processing of Digital Evidence: Filesystem Support for Digital Evidence Bags," Research Advances in Digital Forensics, Springer, 2006.

  28. Bluepipe: On the Spot Digital Forensics Bluepipe Patterns Y. Gao, G. G. Richard III, V. Roussev, “Bluepipe: An Architecture for On-the-Spot Digital Forensics,” International Journal of Digital Evidence (IJDE), 3(1), 2004.

  29. <BLUEPIPE NAME=”findcacti”> <!-- find illegal cacti pics using MD5 hash dictionary --> <DIR TARGET=”/pics/” /> <FINDFILE USEHASHES=TRUE LOCALDIR=”cactus” RECURSIVE=TRUE RETRIEVE=TRUE MSG="Found cactus %s with hash %h "> <FILE ID=3d1e79d11443498df78a1981652be454/> <FILE ID=6f5cd6182125fc4b9445aad18f412128/> <FILE ID=7de79a1ed753ac2980ee2f8e7afa5005/> <FILE ID=ab348734f7347a8a054aa2c774f7aae6/> <FILE ID=b57af575deef030baa709f5bf32ac1ed/> <FILE ID=7074c76fada0b4b419287ee28d705787/> <FILE ID=9de757840cc33d807307e1278f901d3a/> <FILE ID=b12fcf4144dc88cdb2927e91617842b0/> <FILE ID=e7183e5eec7d186f7b5d0ce38e7eaaad/> <FILE ID=808bac4a404911bf2facaa911651e051/> <FILE ID=fffbf594bbae2b3dd6af84e1af4be79c/> <FILE ID=b9776d04e384a10aef6d1c8258fdf054/> </FINDFILE> </BLUEPIPE>

  30. Distributed Digital Forensics 300GB 300GB 750GB 750GB V. Roussev, G. G. Richard III, "Breaking the Performance Wall: The Case for Distributed Digital Forensics,“ Proceedings of the 2004 Digital Forensics Research Workshop (DFRWS 2004), Baltimore, MD

  31. Distributed Digital Forensics • Scalable • Want to support at least IMAGE SIZE / RAM_PER_NODE nodes • Platform independent • Want to be able to incorporate any (reasonable) machine that’s available • Lightweight • Horsepower is for forensics, not the framework—less fat • Highly interactive • Extensible • Allow incorporation of existing sequential tools • e.g., stegdetect, image thumbnailing, file classification, hashing, … • Robust • Must handle failed nodes smoothly

  32. Distributed Digital Forensics (2)

  33. RAID: 504GB RAID: 504GB File Server File Server SCSI SCSI CPU: 2x1.4GHz CPU: 2x1.4GHz Xeon Xeon RAM: 2GB RAM: 2GB Switch Switch 1Gb 1Gb 96 96 - - port, 10/100/1000 Mb port, 10/100/1000 Mb 24 24 Gb Gb Backplane Backplane Node Node CPU: 2.4 GHz CPU: 2.4 GHz Pentium 4 4 RAM: 1 GB RAM: 1 GB Distributed Digital Forensics (3)

  34. Beowulf [RIP], Slayer of Computer Criminals…

  35. Live string search: “Vassil Roussev” Regular expression search: v[a-z]*i[a-z]*a[a-z]*g[a-z]*r[a-z]*a DDF: Results (1)

  36. DDF: Results (2) • Stego detection using Stegdetect 0.5 under RH9 Linux on the cluster • Traditional: • 6GB image mounted using loopback device • find /mnt/loop –exec ./stegdetect ‘{}’ \; • 790 seconds == 13:10 minutes • Using the distributed framework • Stegdetect 0.5 code incorporated into framework • Detection against cached files • “STEGO” command (after IMAGE/CACHE) • 82 seconds == 1:22 minutes • 9.6X faster with 8 machines • CPU bound operation

  37. DDF: To Do List • User interface! (unless you love Putty)

  38. DDF: To Do (2) • Case persistence • Secure support for overlapping cases • Better fault tolerance • Intelligent caching schemes to support larger images • Collaboration with colleagues (you?) working in: • Image analysis/classification • Speech recognition • More stego • Other CPU horsepower-intensive, forensics-applicable stuff • We provide cycles…you provide…

  39. Current: Live Forensics • Physical memory dumps • Hard to do when adversarial OS is present • Via USB hacking? • Firewire proof of concept developed by Maximillian Dornseif • Defeating process hiding techniques, e.g., FU “rootkit” • Check OS components from many angles • Remnants of applications (executed) past… • e.g., instant messenger fragments • e.g., recent invocations of process hiding • e.g., fingerprints of recently executed (or executing) malware

  40. Conclusion: Lots of Work To Do • Benevolent hacking (engineering) meets science • Desperately need methods for pipelining investigative process • Live forensics critically important • volatile computing • whole disk encryption • hardware-based whole disk encryption! • nasty malware

  41. Conclusion (2) • Arguably, almost any field in CS can collaborate • All media handling needs work • Algorithms for dealing with huge, partially-organized datasets • Attribution • Correlation • Profiling • Document similarity measures • Databases • High-performance computing • OS Internals

  42. Random Bedside Reading… • http://www.dfrws.org (Digital Forensics Research Workshop) • http://www.ijde.org/ (International Journal of Digital Evidence) • F. Adelstein, “Live Forensics: Diagnosing Your System Without Killing it First,” Communications of the ACM, February 2006. • M. A. Caloyannides, Privacy Protection and Computer Forensics, Second Edition, 2004. • B. Carrier, File System Forensic Analsis, Addison-Wesley, 2005. • B. Carrier, “Risks of Live Digital Forensics Analysis,” Communications of the ACM, February 2006. • E. Casey, Digital Evidence and Computer Crime, Academic Press, 2004. • J. Chow, B. Pfaff, T. Garfinkel, M. Rosenblum, “Shredding Your Garbage: Reducing Data Lifetime Through Secure Deallocation,” 14th USENIX Security Symposium, 2005. • M. Geiger, “Evaluating Commercial Counter-Forensic Tools,” 5th Annual Digital Forensic Research Workshop (DFRWS 2005), New Orleans, 2005. • G. G. Richard III, V. Roussev, "Next Generation Digital Forensics," Communications of the ACM, February 2006. • G. G. Richard III, V. Roussev, “Digital Forensics Tools: The Next Generation,” invited chapter in Digital Crime and Forensic Science in Cyberspace, IDEA Group Publishing, 2005. • A. Schuster, “Searching for Processes and Threads in Microsoft Windows Memory Dumps,” 6th Annual Digital Forensic Research Workshop (DFRWS 2006), West Lafayette, IN, 2006. • S. Sparks, J. Butler, “Raising the Bar for Windows Rootkit Detection,” Phrack Issue # 63. • G. Hoglund, J. Butler, “Rootkits: Subverting the Windows Kernel,” Addison-Wesley, 2005.

  43. ? Presentation available: http://www.cs.uno.edu/~golden/teach.html golden@cs.uno.edu Security Lab (NSSAL): Math 322

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