Splunk Stacking Redline and MIR host-based forensic artifacts

By Tony Lee, Max Moerles, Ian Ahl, and Kyle Champlin

Introduction

Mandiant’s free forensics tool, Redline®, is well-known for its powerful ability to hunt for evil using IOCs, collect host-based artifacts, and even analyze that collected data.  While this gratis capability is fantastic, it is limited to analyzing data from only one host at a time.  But imagine the power and insight that can be gained when looking at a large set of host-based data; especially when the hosts are standardized using a base build or gold disk image.  This would allow analysts to stack this data and use statistics to find outliers and anomalies within the network.  These discovered anomalies could include:

·         Unique services within an organization (names, paths, service owners)

·         Unique processes within an organization (names, paths, process owners)

·         Unique persistent binaries (names, paths, owners)

·         Drive letters/mappings that don't meet corporate standards

·         Infrequent user authentication (such as forgotten or service accounts)

Any of the above example issues could be misconfigurations or incidents--neither of which should be left unnoticed or unsolved.

Requirements and Prototyping

To solve the stacking problem, we had four major requirements.  We needed a platform that could:

1)      Monitor a directory for incoming data

2)      Easily parse XML data (since both Redline and MIR output evidence to XML)

3)      Handle large files and break them into individual events

4)      Apply “big data” analytics to lots of hosts and lots of data

After looking at the requirements and experimenting a bit, Splunk seemed like a good fit.  We started our prototyping by parsing a few output files and creating dashboards within our freely available side project the Splunk Forensic Investigator App.  The architecture looks like the following:

Figure 1:  Architecture required to process Redline and MIR files within Splunk

We gave this app the ability to process just a few Redline and MIR output files such as system, network, and drivers.  Then we solicited feedback and were pleased with the response.

Results

Since the prototype gained interest, we continued the development efforts and the Splunk Forensic Investigator app now handles the following 15 output files:

System	Network	Processes	Services	Ports
Tasks	Prefetch	ShimCache	DNS	User Accounts
URL History	Driver Modules	Persistence	File Listings	Event Logs

After installation and setup, the first dashboard you will see when processing MIR and Redline output is the MIR Analytics dashboard.  This provides heads up awareness of the number of hosts processed, number of individual events, top source types, top hosts, and much more as shown in Figure 2.

Figure 2:  Main MIR Analytics dashboard

Additionally, every processed output type includes both visualization dashboards and analysis dashboards.  Visualization dashboards are designed flush out the anomalies using statistics such as counts, unique counts, most frequent, and least frequent events.  An example can be seen in Figure 3’s visualization example.

Figure 3:  Example visualization dashboard which shows least and most common attributes

The analysis dashboards parse the XML output from Redline and MIR to display it in a human readable and searchable format.  An example can be seen below in Figure 4.

Figure 4:  Example analysis dashboard which shows raw event data

Conclusion

If you use Redline or MIR and would like to stack data from multiple hosts, feel free to download our latest version of the Splunk Forensic Investigator App.  Follow the instructions on the Splunk download page and you should be up and running in no time.  This work can also be expanded to HX, but it will most likely require a bit of pre-processing by first reading the manifest.json file to determine the contents of the randomized file names.  We hope this is useful for other FireEye/Mandiant/Splunk enthusiasts.

Head nod to the "Add-on for OpenIOC by Megan" for ideas:  https://splunkbase.splunk.com/app/1517/