Incident Response with NTFS INDX Buffers – Part 4: The Internal Structures of an INDX AttributeOctober 16 2012
By Jeff Hamm & William Ballenthin
On August 30th, 2012, we presented a webinar on how to use INDX buffers to assist in an incident response investigation. You can review an archived recording of this presentation here. This four part blog post series addresses questions we were not able to get to during the presentation. This post is the fourth, and final, posting of the series. The four installments are:
- Part 1: Extracting an INDX Attribute
- Part 2: The Internal Structures of a File Name Attribute
- Part 3: A Step by Step Guide to Parse INDX
- Part 4: The Internal Structures of an INDX Structure
In part 1 of the series, we discussed how to extract the INDX attribute from an MFT record during a forensic investigation. In Part 3, our example illustrated how you can use the tool INDXParse.py to recover evidence of deleted files from an NTFS volume. This post will build on Part 2 of the series, which covered the structure of another NTFS attribute: the filename attribute.
Part 4: The Internal Structures of an INDX Attribute
Recall that NTFS structures the Master File Table ($MFT) into small segments, called records, each of which describes one file or directory. Figure 1 diagrams the layout of the $MFT in an NTFS volume. Each $MFT record contains attributes, which store the metadata for the record. When these $MFT records correspond to directories, the record tracks the contents of the directory. INDX attributes are the sub-structures that track the contents of a directory.Figure 1: NTFS Volume Layout Showing the $MFT
Purpose and Structure of the INDX Attribute
The NTFS driver structures an INDX attribute in such a way that the driver can quickly look up if and where a particular file exists. NTFS optimizes for locating files across a large volume, rather than performing efficient file listings. In fact, this is why it can take a while to open a large directory in Explorer (such as C:WINDOWSSystem32), however, launching a program found deep in the file system does not.
Specifically, the attribute places INDX records in a B+ tree, where the key is the filename. A B+ tree is a data structure where arbitrary records are organized by a sortable key value, such as a number or a string. Each node in the tree may have many branches that lead to further nodes. Locating the data associated with a key value boils down to traversing the nodes and using the key value to decide which branch to take. For a forensic investigator, the effect of the B+ tree is that INDX records associated with a node are stored as a chunk in alphanumeric order.
The size of a B+ node is a parameter to the NTFS file system; however, it is typically 4096 bytes. This means that the size of an INDX allocation attribute will always be a multiple of 4096 bytes. Within the node, INDX records are arranged consecutively with no space between neighboring records. Since there are usually fewer records than a node's full capacity, there is often slack space between the last INDX record and the end of the B+ tree node. Figure 2 diagrams the layout of an INDX allocation attribute.Figure 2: INDX Allocation Attribute Layout
When a file is added to a directory, a new record is added to the INDX attribute of the directory. Within the B+ tree, NTFS finds the appropriate node and inserts the new record --- shifting records down, if necessary. When this happens, the slack space at the end of the node shrinks. When a file is deleted from a directory, the opposite operation happens, and NTFS removes an INDX record from the B+ tree node. During this removal, NTFS shifts records back up to fill the newly created space.
During INDX operations, the Microsoft Windows NTFS driver does not rewrite the entire B+ tree node. Instead, the driver only modifies the addresses of active INDX records found alphanumerically after the deleted record. Due to this, Windows does not zero out the slack space, and an investigator may recover old copies of INDX records from this area. When INDXParse.py recovered metadata of deleted files in Part 3, it parsed unused copies of INDX records from a node's slack space. Figure 3 diagrams the behavior of the Microsoft NTFS driver as an INDX record is deleted. When the driver removes INDX record "F", it shifts the records "G" and "H" to fill the space. As the contents of record "H" shift, a recoverable copy (inactive record "H' ") remains in the newly expanded slack space.Figure 3: Microsoft NTFS Behavior as an INDX Record is Deleted
Purpose and Structure of the INDX Record
By now, we know that an INDX record is an entry in a B+ tree that allows NTFS to quickly look up the contents of a directory. The INDX record contains data that describes one file or subdirectory in the INDX attribute of a parent directory. As it turns out, the structure of this record is familiar to us.
The structure of an INDX record is the same as an NTFS filename attribute, wrapped with two extra values. In part 2, we reviewed the information and layout of data stored in a filename attribute. The INDX record structure reuses the layout of the filename attribute, including the filename field, the file timestamp fields, and file size field. In addition to the filename attribute structure, the INDX record starts with a reference to the $MFT record of the file and a set of descriptive flags. Figure 4 lists the filename attribute structure formatted as a C type, while Figure 5 l lists the INDX record structure.Figure 4: C Structure for the NTFS Filename AttributeTable 1: Structure for the NTFS File Name AttributeFigure 5: C Structure for an INDX RecordTableTable 2: Structure for an INDX Record
In this blog post series, we discussed how NTFS uses INDX attributes to track the contents of a directory. As forensic investigators, we can exploit the behavior of the Microsoft Windows driver to recover information about deleted files from a file system. This is a powerful technique in our arsenal, so Parts 1 and 3 documented exactly how to use the appropriate forensic tools. In this final post, we dug deep into the binary structure of INDX attributes to explain the patterns you may see during an investigation. We hope you enjoyed this series!