Layering Data

Monday, January 21, 2013 Posted by Corey Harrell 3 comments
Layering is defined as the action of arranging something into layers. There are various reasons to why data is layered but I think the most important one is to show a more accurate picture about something. Each layer may contain different information so when the layers are combined all of the information can be seen. Providing a more accurate picture about something even applies in Digital Forensics and Incident Response (DFIR). I saw its benefits when layering different artifacts showing similar information such as the installed software artifacts. A single artifact may not show all of the software that is or was installed but looking at the information from all artifacts provide a more accurate picture of what programs were on a computer. I have found layering data to be a powerful technique; from what programs executed to what files were accessed to what activity occurred on a system. I hope to demonstrate the benefits to layering data through the eye of a timeline.

Layering Data in Action


Before diving into timelines I wanted to take a step back to first illustrate layering data. The best visual example of how layering data provides a more accurate picture of something is the way mapping software works. All layers contain information about the same geographical location but the data each layer contains is different.

I wanted to use an example closer to home to show the additional information layering data in maps provides. When my wife and I were looking for a house to buy one of the things we took into consideration was the crime rate in the neighborhood. We didn’t want to end up in a rough neighborhood so we wanted additional information about the neighborhoods we were looking at. Unfortunately, there are no online crime maps where I live so I had to settle for the City of Spokane, Washington Crime Map I found with a Google search. Let’s say my wife and I were looking at a loft in downtown Spokane located in the red box on the map.


Using the crime map I first wanted to know what burglaries occurred over the past month.


So far so good; there were a few burglaries that occurred but none were inside the red box. A month doesn’t provide an accurate picture; let’s see the statistics for the past year.


Adding this additional layer provides more information about the burglaries in the area. Like most people, we are more worried about all crime as opposed to just one type of crime. Adding the all crime layer to the map provides even more information.


The new layer provides more information about the crime in the downtown area but adding another layer will provide more even more context. I added the heat map layer as shown below.


The heat map layer now shows an accurate picture about the crime rate around downtown Spokane where our imaginary loft is located. The loft is located in the area that has the highest concentration of crime. By layering data on top of the geographic location we were interested in would enable us to make a more informed decision about if we would actually want to live there. Please note: I only used Spokane since it was the first crime map I saw on a Google search. I have no knowledge about the downtown area and it might be a great place to live.

Layering Data in Timelines


System timelines are a great way to illustrate layering data in DFIR. Artifacts can be organized into layers and then applied to a timeline as a group. The easiest way to see this is by looking at the examination process I use. Below are a few of my examination steps:

        - Examine the programs ran on the system
        - Examine the auto-start locations
        - Examine the host-based logs
        - Examine file system artifacts

I tend to group artifacts together underneath the examination step they pertain to. In other words, I organize all artifacts based on categories that match my examination step. For example, the files listed underneath the examine file system artifacts step include: $MFT, $LogFile, $UsnJrnl, and $INDX files. When I want to know something about the file system then I will examine all of these artifacts. I discussed this examination approach before when I wrote about how to obtain information about the operating system (side note: I updated the script and it automates my entire registry examination process). Harlan shared his thoughts about the usefulness of categorizing artifacts in his post There Are Four Lights: The Analysis Matrix. SANs released their DFIR poster which organizes artifacts based on categories. In my opionion this is the best technqiue when approahing an examination and to deomonstrate it I’ll use the image from the post Extracting ZeroAccess from NTFS Extended Attributes.

In the post I mentioned how ZeroAccess modified the services.exe file so it loads the Trojan from the NTFS Extended Attributes. I parsed the $MFT with AnalyzeMFT. The services.exe file was timestomped using file system tunneling; I focused on the timestamp for the last MFT update which was 12/06/2012 22:18:06.


The $MFT by itself provides a wealth of information but it doesn’t provide any historical information. This is where layering data comes into play and the other NTFS artifacts. I parsed the $LogFile with David Cowen’s Advanced NTFS Journal Parser public version and added it to the timeline (check out his other post Happy new year, new post The NTFS Forensic Triforce to see how the NTFS artifacts tie together).


The $Logfile provided a little more context about the time when the services.exe $MFT record was last updated. The rows in blue shows a file was renamed followed by services.exe being created. Let’s continue layering data by adding the information stored in the $UsnJrnl file. I parsed the file with Tzwork’s Windows Journal Parser and added it to the timeline.


The $UsnJrnl also shows the services.exe file was renamed before it was created as well as other changes made to the file’s attributes.

Summary


The timeline only contained one layer of artifacts which were the NTFS artifacts. Combining the information stored in the $MFT, $LogFile, and $UsnJrnl provided more context about the services.exe file and how it came to be. Even more information could be obtained by adding more layers to the timeline such as program execution and logging information. Layering data in DFIR should not be limited to timelines. Every artifact can be organized into categories and the categories themselves can be treated as layers of information.

Re-Introducing $UsnJrnl

Tuesday, January 1, 2013 Posted by Corey Harrell 14 comments
The NTFS change journal ($UsnJrnl) is not a new artifact and has been discussed before by others. The file's importance may have been overlooked since it wasn’t available in Windows XP by default. As more and more systems running newer Windows operating systems cross our desks it is crucial to know about the $UsnJrnl file. This is because starting with Windows Vista and continuing into Windows 7 the operating systems use the change journal. The $UsnJrnl file contains a wealth of information about file system activity which can provide more context about what occurred on a system. The information can even counter anti-forensics techniques leveraged by malware to subvert post-mortem examinations. Read on to get reacquainted with this NTFS artifact.

What is the $UsnJrnl File?


According to the book File System Forensic Analysis, the change journal “is a file that records when changes are made to files and directories”. The change journal is located at $Extend\$UsnJrnl and the actual journal entries are stored in the $UsnJrnl:$J alternate data stream. The Windows Internals Part 2: Covering Windows Server 2008 R2 and Windows 7 book details the information contained in each entry:

     - Time of change
     - Reason for the change
     - File/directory’s name
     - File/directory’s attributes
     - File/directory’s MFT record number
     - File record number of the file’s parent directory
     - Security ID
     - Update Sequence Number of the record
     - Information about the source of the change

Others have already highlighted the $UsnJrnl file’s forensic significance. One of which was Bugbear over at the Security Braindump. In his post Dear Diary: AntiMalwareLab.exe File_Created he not only goes into detail about the $UsnJrnl file structure but he even shared how the artifact was beneficial on a case. The change journal’s significance is not limited to malware type cases; it’s relevant to any type of case where it is important to know what changes occurred to the NTFS file system. Case in point, on a fraud case the $UsnJrnl file showed how a user performed a mass deletion of financial files.

Malware Anti-Forensic Techniques


There are various definitions explaining what anti-forensics is. The one I tend to prefer is Marcus Rogers where he stated anti-forensics are “attempts to negatively effect the existence, amount and/or quality of evidence from a crime scene, or make the analysis and examination of evidence difficult or impossible to conduct”. The Rootkit Arsenal 2nd Edition goes on to explain that the goal of anti-forensics is to “minimize the quantity of useful trace evidence that’s generated in addition to ensuring that the quality of this information is also limited (as far as a forensic investigation is concerned).” Malware can leverage anti-forensics to undermine examinations conducted post mortem and live as well as against the malware itself. As it relates to post mortem examinations, two anti-forensic strategies are data hiding and data destruction; both of which can be countered using the information in the $UsnJrnl file.

Data Hiding in Obscure Location


As the name implies, data hiding is a technique to store data where it is unlikely to be found. Remember the Rootkit Paradox and the first part of the predicament; they want to remain hidden. One area malware tries to hide is in obscure locations on the system with hopes that people won’t notice it.

Some ZeroAccess Trojan variants (i.e. MD5 3dbb09aa9b6eff5a180b3412b54e9862) use the recycle bin as a hiding location. The best way to illustrate this is to use a hypothetical scenario where an IDS alert provided the lead to use when examining the $UsnJrnl:$J file. Hypothetically, let’s say an IDS alert went off around 12/14/2012 15:38 UTC for Zeroaccess Outbound UDP traffic. The $J file was parsed with TZwork’s Windows Journal Parser (command used was: jp64.exe –file $J) and the output showed the following at the time of interest:


The output shows at 15:38:30 the following items were created: $5da39e9580074308c6cfbcce61795d0d, L, U, @, and n. The $J file entries only contain file/directory names but the MFT entry can be used to obtain the full file path. The first item of interest is $5da39e9580074308c6cfbcce61795d0d and its MFT entry is 0x00000000a477 (converted to decimal the value is 42103). Locating the MFT record 42103 in the parsed $MFT showed the following:


The item’s full path was /$Recycle.Bin/S-1-5-21-2793522790-2301028668-542554750-1000/$5da39e9580074308c6cfbcce61795d0d. Correlating the MFT entries listed in the $J file for the L, U, and @ files shows they were located inside the $5da39e9580074308c6cfbcce61795d0d directory. The information in the $J file was able to reveal the hiding spot used by this ZeroAccess variant.

Data Destruction


Data destruction is a technique where data is deleted to limit the amount of forensic evidence left on a system. The technique makes it more challenging to determine what happened. Two data destruction techniques to thwart post-mortem forensics are self deleting droppers/downloaders and overwriting file system metadata.

Self Deleting Droppers/Downloaders

A dropper is a type of program when executed will install malicious components. Typically, the malicious components are embedded within the dropper itself. A downloader on the other hand is a program when executed will download and install malicious components. One anti-forensic technique leveraged by droppers and downloaders is to delete the initial executable after its done its job. By deleting the executable it removes evidence about the initial infection vector. One issue faced with self deletion is that Windows does not allow for a process to delete itself. To get around this issue malware can use a self dissolving batch script or another type of shell script. Basically, the malicious process will spawn a new shell process (such as the command prompt) and the shell process will perform the deletion. The ZeroAccess Trojan MD5 3dbb09aa9b6eff5a180b3412b54e9862 is a self deleting dropper and the image below is the portion of the $J file where the Trojan (9862.exe) was deleted.


The change journal logged the 9862.exe file being deleted followed by the cmd.exe prefetch file being modified. The most interesting thing is over time the MFT record for the 9862.exe file will be reused and the cmd.exe prefetch file will change; both of which may limit the ability to determine the 9862.exe file was self deleting. However, the change journal may still maintain entries showing what occurred on the system when other artifacts no longer do.

Overwriting File System Metadata

The timestamps stored in the NTFS metadata are crucial when trying to determine what happened on a system. The metadata includes timestamps which are used to construct a timeline of activity showing how the events unfolded that resulted in a compromise. One anti-forensic technique leveraged by malware is to overwrite the timestamps making it harder to build a timeline. The ZeroAccess Trojan MD5 b437274492e215c25f96711d04b44050 uses two different methods to over write timestamps.

The $MFT maintains two sets of timestamps for files and directories; the Standard Information (SIA) and Filename Attribute (FNA). Most times when timestamps are overwritten it only impacts the SIA timestamps leaving the FNA timestamps intact. Comparing the FNA timestamps to the SIA timestamps is an effective way to determine if timestamps have been altered. To illustrate the following are the timestamps from the file /Users/lab/AppData/Local/{5da39e95-8007-4308-c6cf-bcce61795d0d}/n

Standard Information Attribute

Creation:       7/13/2009 23:11:59 UTC
Access:         7/13/2009 23:11:59 UTC
Modification:  7/14/2009 1:17:52 UTC


Filename Attribute

Creation:       12/6/2012 22:18:00 UTC
Access:         12/6/2012 22:18:00 UTC
Modification:  12/6/2012 22:18:00 UTC

The n file’s timestamps have been altered making it appear as if the file was created and last modified back in 2009. The $J file provides another method to determine if a file’s timestamps were altered since it records when items are created. The image below shows the $J file entry when the n file was created.


As can be seen, the entry reveals the n file was created on 12/6/2012 at 22:18:00 UTC as opposed to 7/13/2009 23:11:59 UTC which was reflected in the SIA timestamp . To detect timestamp manipulation the file/directory’s creation date can be compared to its record in the $J file.

     File System Tunneling

When I was looking at the artifacts associated with the ZeroAccess Trojan levering the NTFS Extended Attributes I quickly noticed how the malware altered its files’ timestamps. I even found the activity in the $J file showing the services.exe file being modified. However, the activity didn’t become clear until I shared the $MFT file with Harlan. Harlan was parsing the $MFT file when he noticed the Trojan used file system tunneling to alter the services.exe timestamps. Jason Hale over at The Digital Forensics Stream post File System Tunneling in Windows does an outstanding job demonstrating how file system tunneling works. Microsoft’s explanation about file system tunneling in Windows is to:

“enable compatibility with programs that rely on file systems being able to hold onto file meta-info for a short period of time. This occurs after deletion or renaming and re-introducing a new directory entry with that meta-info (if a create or rename occurs to cause a file of that name to appear again in a short period of time).”

File system tunneling means if a file is renamed or deleted and then if a file with the same name is created in the same directory then the new file will retain the timestamps from the old file. The $UsnJrnl file contains entries showing file system tunneling was used and this is what became clear after Harlan told me what he saw.


The entries show that the original services.exe file was renamed and within a fraction of a second a new services.exe file was created in the same directory. As shown below the new services.exe file retained the timestamps from the original:

Standard Information Attribute

Creation:       7/13/2009 23:11:26 UTC
Access:         7/13/2009 23:11:26 UTC
Modification:  7/14/2009 1:14:36 UTC


Filename Attribute

Creation:       7/13/2009 23:11:26 UTC
Access:         12/6/2012 22:18:06 UTC
Modification:  12/6/2012 22:18:06 UTC

Both the SIA and FNA timestamps indicate the services.exe file was created back in 2009 but the entries in the $J file shows otherwise. The services.exe was actually created on 12/6/2012 and file system tunneling was used to alter its timestamps.

Summary


The $UsnJrnl file contains a wealth of information about file system activity and its worthy for us to re-introduce ourselves to this artifact. This re-introduction shouldn’t be viewed in the same light as when Jay Z reintroduced himself. Jay Z was saying he was the top MC when he said “allow me to re-introduce myself. My name is Hov’, OH, H to the OV”. The $UsnJrnl is only one of the available NTFS artifacts; it’s not even the only artifact that logs file system activity. The $Logfile is another NTFS artifact with entries recording activity. David Cowen has been releasing information about the $Logfile and the program he wrote to parse it. A few posts include: Updates and DFIR Conferences discussing what the NTFS $Log Parser is and PFIC 2012 Slides & Bsides DFW containing a link to his slide deck where he breaks down the $Logfile. I highly recommend reading what David posted if you haven’t already. The $Logfile is another artifact that contains a wealth of information. The limited testing I did showed it contains information which wasn’t present in the $MFT and $UsnJrnl files. And vice versa about the $UsnJrnl compared to the $Logfile and $MFT. To get an accurate picture about file system activity then all the artifacts ($MFT, $Logfile, and $UsnJrnl) should be examined. Combining the information inside each artifact will counter anti-forensics techniques leveraged by malware to thwart post-mortem examinations.