Splunk and McAfee ePO Integration – Part II

By Tony Lee

In our previous article we outlined one method to integrate McAfee's ePolicy Orchestrator (ePO) with Splunk’s flexible Workflow actions. This allows SOC analysts to task ePO directly from Splunk. In this article, we will highlight a different and potentially more user friendly method. For convenience we have integrated this dashboard into version 1.1.8 of the Forensic Investigator app (Toolbox -> ePO Connector).

Forensic Investigator app ePO connector tool

As with the previous article, all that’s needed is the following:

• Administrator access to Splunk
• URL, port, and service account (with administrator rights) to ePO

Testing the ePO API and credentials

It still may be useful to first ensure that our ePO credentials, URL and port are correct. Using the curl command, we will send a few simple queries. If all is well, the command found below will result in a list of supported Web API commands.

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/core.help

If this failed, then check your credentials, IP, port, and connection. Once the command works, try the following to search for a host or user:

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/system.find?searchText=<hostname/IP/MAC/User>

Splunk Integration

The Forensic Investigator ePO connector dashboard contains the following ePO capabilities:

• Query
• Wake up
• Set tag
• Clear tag

This allows users to query for hosts using a hostname, IP addrses, MAC address, or even username. Then users can set a tag, wake the host up, and even clear a tag.

Setup

1)  Download and install
Before this integration is possible, first install the Forensic Investigator app (version 1.1.8 or later).

2)  CLI edit
Then edit the following file:

$SPLUNK_HOME/etc/apps/ForensicInvestigator/bin/epoconnector.py

Set the following:  IP, port, username, and password

theurl = 'https://<IP>:8443/remote/'
username = '<username>'
password = '<password>'

3)  Web UI dashboard edit

The dashboard is accessible via Toolbox --> ePO Connector.  There is a Quarantine tag present by default, but others can be added via the Splunk UI by selecting the edit button on the dashboard.

Lingering concerns

Using this integration method, there are a few remaining concerns:

• The ePO password is contained in the epoconnector.py python script
• Fortunately, this is only exposed to Splunk admins.
• Let us know if you have another solution.  :-)
• ePO API authentication uses Base64.  The resulting URL can be modified and it will still be authenticated and will issue commands to ePO.
• SSL should be used with the ePO API to protect the communications
• Limit this dashboard to only trusted users.
• Leaving the system.find searchText parameter blank returns everything in ePO
• ePO seems resilient even to large queries.  We also filtered out blank queries in the python script.

 

Conclusion 

This second ePO integration method should be quite user friendly and can be restricted to those who only need access to this dashboard. It could also be used in conjunction with out previous integration method too. Enjoy!

Splunk and McAfee ePO Integration – Part I

By Tony Lee

Have you ever wanted to task McAfee ePolicy Orchestrator (ePO) right from Splunk? Lucky for us, ePO has robust Web API scripting capabilities and is well-documented in a reference guide found here:

https://kc.mcafee.com/resources/sites/MCAFEE/content/live/PRODUCT_DOCUMENTATION/24000/PD24810/en_US/epo_510_rg_Web%20API_0-00_en-us.pdf

Combine this with Splunk’s flexible Workflow actions and we have the basic building blocks to allow SOC analysts to task ePO. All that’s needed is the following:

• Administrator access to Splunk
• URL, port, and service account (with administrator rights) to ePO

Testing the ePO API and credentials

In order to start the integration, let’s first ensure that our credentials, URL and port are correct. Using the curl command, we will send a few simple queries. If all is well, the command found below will result in a list of supported Web API commands.

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/core.help

If this failed, then check your credentials, IP, port, and connection. Once the command works, try the following to search for a host or user:

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/system.find?searchText=<hostname/IP/MAC/User>

Pro Tips:

• Do not leave the searchText parameter blank or it will return everything in ePO.
• Machine readable output such as XML or JSON may be desired. 

To return XML or JSON, use the :output parameter as shown in the example below:

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/system.find?:output=json&searchText=<hostname/IP/MAC/User>

Our use case

There are many ways in which a SOC could benefit from Splunk/ePO integration. However, in this use case, we have ePO configured to perform automated actions (such as run a full AV scan) when certain tags are applied to hosts. Now wouldn’t it be convenient if we could tell Splunk to have ePO apply the tag to kick off the actions? How about clearing tags?  Both actions are exposed through ePO’s API:

Apply a tag: /remote/system.applyTag?names=<Host>&tagName=FullAVScan

Clear a tag: /remote/system.clearTag?names=<Host>&tagName=FullAVScan

Splunk Integration

One possible integration leverages Splunk’s Workflow Actions to issue these web API commands to ePO. This allows the analyst to pivot from the Event screen in a search using the Event Actions button as shown in the screenshot below:



Splunk’s Workflow actions can be setup using the following:
Settings -> Fields -> Workflow Actions -> Add New

(Note:  This example uses the field Hostname field to identify the asset, change this to match your field name):

Name:  FullAVScan
Label:  Run a FullAVScan on $Hostname$
Apply only to the following fields:  Hostname
Apply only to the following event types:  left blank 
Show action in: Both
Action type:  link
URI:  https://<User>:<Password> @<EPOServer>:<EPOport>/remote/system.applyTag?names=$Hostname$&tagName=FullAVScan
Open link in:  New window
Link method:  get

Note:  You may need to restart Splunk to make sure the Workflow Actions appear in the Event Actions drop down.

Security mitigations

This integration obviously exposes a lot of power to the Splunk analysts and potential attackers if Splunk is compromised.  Here are some steps that can be taken to limit abuse:

• Create a specific service account in ePO for Splunk to use, do not reuse a user account
• Limit access to the Workflow Action to a small group
• Set a Network IP filter for the ePO/Splunk account to block any IP from using that account except the Splunk search head

Results:
The results that are returned from ePO depend on the action performed, however the message seems consistent.  See below for example messages for both a successful tasking and unsuccessful tasking.

Successful tasking:

OK: 1

Unsuccessful tasking:

OK: 0

 

Other possibilities

We have demonstrated the ability to query ePO for information by using hostname, IP address, MAC address, and user.  We also showed how it is possible to apply and remove tags.  But what else is possible?  You could ask ePO using the first test command used at the beginning of this article.

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/core.help

OK: ComputerMgmt.createCustomInstallPackageCmd windowsPackage deployPath [ahId] [fallBackAhId] [useCred] [domain] [username] [password] [rememberDomainCredentials] ComputerMgmt.create.Custom.Install.Package.Cmd.short-desc agentmgmt.listAgentHandlers - List all Agent Handlers clienttask.export [productId] [fileName] - Exports client tasks clienttask.find [searchText] - Finds client tasks clienttask.importClientTask importFileName – Imports --snip--

To obtain help on a specific API command, but the following syntax with the command parameter:

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/core.help?command=core.listQueries

OK: core.listQueries Displays all queries that the user is permitted to see. Returns the list of queries or throws on error. Requires permission to use queries.

Troubleshooting

If issues arise, just use the curl command to eliminate complexity.  Verify credentials, IP, port, and connectivity, then move on to more complicated integration.

curl -v -k -u <User>:<Password> "https://<EPOServer>:<EPOPort>/remote/core.help

Lingering concerns

Using this integration method, there are a few remaining concerns.  There are:

• The ePO password is contained in the Splunk Workflow setup screen
• Fortunately, this is only exposed to Splunk admins.
• ePO API authentication uses Base64.  The resulting URL can be modified and it will still be authenticated and will issue commands to ePO.
• SSL in ePO should be used to protect the data
• Leaving the system.find searchText parameter blank returns everything in ePO
• ePO seems resilient even to large queries

  

Conclusion

This is just one example of what can be done when integrating Splunk and McAfee ePO. In our next article we will discuss further integration options using a little python and simple XML. We hope this was useful if you are ever tasked with integrating these two technologies.

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/ 

Event acknowlegement using Splunk KV Store

By Tony Lee

Introduction

Whether you use Splunk for operations, security, or any other purpose--it can be helpful to be able to acknowledge events and add notes.  Splunk provides a few different methods to accomplish this task:  using an external database, writing to files, or the App Key Value Store (aka KV Store).  The problem with using an external database is that it requires another system to provision and protect and can add unwanted complexity.  Writing to files can be problematic in a distributed Splunk architecture that may use clustered or non-clustered components.  The last option is the Splunk KV Store which appears to be the current recommendation from Splunk, but this can also appear complex at first--thus we will do our best to break it down in this article.

In the most basic explanation, the KV Store allows users to write information to Splunk and recall it at a later time.  Furthermore, KV Store lookups can be used to augment your event data by mapping event fields to fields assigned in your App Key Value Store collections. KV Store lookups can be invoked through REST endpoints or by using the following SPL search commands: lookup, inputlookup, and outputlookup.  REST commands can require additional permissions, so this article will look at possibilities using the search commands.

References

Before we get started, we will list some references that helped in our understanding of the Splunk KV Store:
http://docs.splunk.com/Documentation/Splunk/latest/Knowledge/ConfigureKVstorelookups
http://docs.splunk.com/Documentation/Splunk/latest/SearchReference/Outputlookup
http://docs.splunk.com/Documentation/Splunk/latest/SearchReference/Inputlookup
http://docs.splunk.com/Documentation/Splunk/latest/SearchReference/Lookup

Deciding on the fields

For this example, we wanted to add a couple of fields to augment our event data.  Namely an acknowledgement field (we will call this Ack) and a notes field (we will call this Notes).  We will match the unique event id field with a field that is also called id.

So, in summary, we have id, Ack, and Notes.  Splunk also uses an internal _key field, but we will not reference this directly in our efforts.

Getting started

Per our references above on configuring KV Store lookups, we will need two supporting configurations:

1. A collections.conf file specifying our collection name
2. A stanza in transforms.conf to specify kvstore parameters

cat collections.conf 

#

# Splunk app KV Store collection file

#

[acknotescoll]

head transforms.conf 

[acknotes]

external_type = kvstore

collection = acknotescoll

fields_list = _key, id, Ack, Notes

Interacting with KV Store using search

The reference links provide helpful examples, but they do not provide everything necessary.  Some of this was discovered through a bit of trial and error.  Especially the flags and resulting behavior.  We list below the major actions that can be taken and the search commands necessary to perform those actions: 

Write new record:
| localop | stats count | eval id=101 | eval Ack="Y" | eval Notes="These are notes for event 101"| outputlookup acknotes append=True

Note:  Without append=True, the entire KV Store is erased and only this record will be present


Update a record (only works if the record already exists):
| inputlookup acknotes where id="100" | eval Ack="N" | eval Notes="We can choose not to ack event 100" | outputlookup acknotes append=True

Note:  Without append=True, the entire KV Store is erased and only this record will be present

Read all records:
| inputlookup acknotes


Read a record (A new search):
| inputlookup acknotes where id="$id$" | table _key, id, Ack, Notes

Read a record (combined with another search):
<search> | lookup acknotes where id="100" | table _key, id, Ack, Notes

Limitation and work around

Unfortunately, it does not look like Splunk has a single search command/method to update a record, but create the record if it does not already exist.  I may be mistaken about this and hope that I am missing some clever flag, so feel free to leave comments in the feedback section below.  To get around this limitation, we first created a "simple" search command to check for the existence of a record.

Determine if record exists:
| inputlookup acknotes where id="108" | appendpipe [stats count | where count==0] | eval execute=if(isnull(id),"Record Does Not Exist","Record Exists!") | table execute

Example of a record that exists

Example of record that does not exist

Conditional update:
Now that we can determine if a record exists and we know how to create a new record and update an existing record, we can combine all three to modify and/or create entries depending on their existence.

<query>| inputlookup acknotes where id="$id$" | appendpipe [stats count | where count==0] | eval execute=if(isnull(id),"| localop | stats count | eval id=$id$ | eval Ack=\"$Ack$\" | eval Notes=\"$Note$\" | outputlookup acknotes append=True","| inputlookup acknotes where id=\"$id$\" | eval Ack=\"$Ack$\" | eval Notes=\"$Note$\" | outputlookup acknotes append=True") | eval kvid=$id$ | eval kvack="$Ack$" | eval kvnote="$Note$" | eval Submit="Click me to Submit" | table kvid, kvack, kvnote, execute, Submit</query>

Results

These are just some examples of what is possible.

You could create an event acknowledgement page

Event acknowledgement page

Once the fields are filled in at the top with the event id, acknowledgement, and notes, it could create the command to either update or add a new entry to the KV Store.  Clicking the Submit hyperlink will actually run that command and modify the KV Store.

Event acknowledgement page filled out and waiting for click to submit

Once the data is populated in the KV Store, these records can be mapped to the original events to add this data for analysts.

Original event data with KV Store augmentation

Conclusion

Hopefully this helps expose some of the interesting possibilities of using Splunk's KV Store to create an event acknowledgement/ticketing system using search operations.  Feel free to leave feedback below--especially if there is an easier search operation for updating a record and adding a new one if it does not already exist.  Thanks for reading.

Forensic Investigator Splunk App - Version 1.1.4

By Tony Lee

Introduction

Our last release, version 1.1.3 was a pretty exciting release with new tools such as the chat program, link extractor, and various monitoring tools.  This time we focused on adding host enumeration tools that can be useful when trying to discover information about a remote host.  In addition, we have added a bulk search option that allows users to search on a list of items such as MD5 hashes, IP addresses or URLs for example.  Here is what we have in store for you in version 1.1.4 which is now available for free via the Splunk App store.

High Level

New Features in v1.1.4
 - Updated Investigator Chat 2.0!
 - Added Ping tool (Host --> Ping)
 - Added SMB Share Viewer (Host --> SMB Share Viewer)
 - Added NetBIOS Viewer (Host --> NetBIOS Viewer)
 - Added Port scanner (Host --> Port Scanner)
 - Added Banner grabber (Host --> Banner grabber)
 - Added Bulk searching of data using any field (Toolbox -> Bulk Search - Wild)
 - Added Bulk searching of data using a specific field (Toolbox -> Bulk Search - Field)
 - Added ASCII Table cheatsheet (Toolbox -> Cheat sheets -> ASCII Table)
 - Added Ports and services cheatsheet (Toolbox -> Cheat sheets -> Ports and Services)
 - Added subnetting cheatsheet (Toolbox -> Cheat sheets -> Subnetting)

Maintenance in v.1.1.4
 - Renamed the xml files to increase simplicity

Investigator Chat 2.0

The chat program received a pretty slick upgrade that makes it much more functional and easier to use.  Big thanks to Kyle for that upgrade.  It now lacks the annoying 5 second refresh rate.

Host Tools

Secure environments will lock down command prompts and restrict access to certain tools--thus it can be useful to have some host enumeration tools that can be activated through Splunk to query remote hosts.

Ping Tool

This is the simplest tool to reach out and see if the host is alive.  The assumption is that ICMP is not blocked at the network or host.

SMB Share Viewer

It can be nice to check for Windows shares as well.  If run from Windows, it will use net view and will not see "hidden" shares (those that end in a $ sign, such as C$, ADMIN$, IPC$).  If run from Linux, it will use smbclient and will see hidden shares.

NetBIOS Viewer

It is also useful to be able to pull NetBIOS table information from a remote host to determine function, users, domain and more.

Port Scanner

Determining the open ports can also be useful for determining the function of a host.  Unfortunately, nmap or other port scanners may not always be available... so we provided a python based port scanner exposed through Splunk.

Banner Grabber

Taking it a step further, we added a python based banner grabber as well.  It should be able to pull most banners, but let us know if it struggles against a particular service.

Bulk Searching - Wild and specific field

Often we have a large list of MD5 hashes, IP addresses, or URLs to run through Splunk.  We could search one item at a time, but that is slow.  We could create a complex boolean statement, but that takes time.  How about just copying and pasting that list into a search field?  Perfect!  This has been tested with Chrome and Firefox which seems to work best.  The file should contain one search item per line.  When copied and pasted into the Splunk Search list field, the browser should separate the terms with spaces.  There are two versions, one which you must specify the field and one that will search all fields (wild).

Cheatsheets - ASCII table, Ports and services, Subnetting

Finally, everyone can use some cheatsheets.  Quick references such as an ASCII table, ports and services, and subnet information.  No more wasting time searching the Internet--especially if you are on a closed network.  These are now local references available in Splunk.

Conclusion

Hopefully you will enjoy the new features of the app.  As always, we appreciate the great feedback we are receiving.  Please send more ideas from within the app using Help --> Send Feedback.

Forensic Investigator Splunk App - Version 1.1.3

By Tony Lee

Introduction

It has been a little while since we released new features in the Forensic Investigator Splunk App, so we are excited about the latest update.  We have received excellent feedback on the app and have also been brainstorming some ideas for new tools to include.  Here is what we have in store for you in version 1.1.3 which is now available for free via the Splunk App store.

High Level

New Features
 - Added a chat program for collaboration!  It is a first stab, but give it a try (Help -> Chat Program)
 - Added an additional whois lookup vendor - api.hackertarget.com - ex: http://api.hackertarget.com/whois/?q=splunk.com
 - Added a link extractor to rip links out of a page (URL/IP -> Link Extractor)
 - Added permalink information to VT lookup page
 - Added disk usage monitor (Help -> Disk Monitor)  (Uses REST API)
 - Added license analysis page (Help -> License Usage) (*Need to have _internal logs on indexer and role based access)

Bug Fixes
 - Fixed VT lookup script, incorrectly detecting MD5 hashes in URL - if (re.findall(r"(^[a-fA-F\d]{32})", sys.argv[1]))
 - Fixed VT Lookup script, removed leading white spaces lstrip()
 - Fixed bug in BulkWhois to provide state/province information

Chat Program

This is a first stab at a collaboration mechanism within Splunk.  It works for a quick and dirty.  The only annoyance is the refresh every 5 seconds.  I am sure it can be made fancier with some Java Script so if you do a little dev and want to contribute--we would appreciate it.

Additional WHOIS vendor

For a while, it appears that bulkwhois had an ISP issue.  Thus, we added a second provider as another option.  Big thanks to hackertarget.com.

Link Extractor

This is useful if you don't want to visit a potentially malicious site, but you want to know the links on the site.  This tool will rip all of the links from the page safely and quickly.

Disk Usage

This last tool is useful for those who need to monitor how much storage is left on their indexers.  This is customizable to your server name and volume that holds indexed data.  By default it is set to my development box which is a simple Kali VM.

Conclusion

Hopefully you will enjoy the new features of the app.  As always, we appreciate the great feedback we are receiving.  Please send more ideas from within the app using Help --> Send Feedback.

Processing Mandiant Redline Files Using Splunk

By Tony Lee

Introduction

Do you use Mandiant's Redline (https://www.fireeye.com/services/freeware/redline.html) for performing host investigation?  Do you use Splunk for centralized log collection and monitoring?  How about using these two tools together?  The team behind the Splunk Forensic Investigator app (https://splunkbase.splunk.com/app/2895/) is experimenting with ingesting Redline collections.  We have made good progress on proving that it is possible to automate the ingestion of Redline collections and use Splunk to carve and display data from multiple hosts at the same time.  However we were wondering how many people would find this capability useful enough to see the work completed.  Check out the prototyping below and let us know if you would find this useful by leaving a comment below (account not necessary).

We have example output below:

System info displayed in Redline

System info displayed in Splunk

Driver modules displayed in Redline

Driver modules displayed in Splunk

Above and beyond replication

Recreating the Redline output is all well and good, however keep in mind that ingesting the data into Splunk allows you to filter, search, and carve across multiple systems at the same time.  Additionally, it would allow you to use Splunk's big data crunching capabilities.  It is very simple to ask Splunk to apply statistical analysis to large data sets to help look for anomalies within hosts such as:

• Drive letters/mappings that don't meet corporate standards
• Logged in/on users that occur infrequently (such as service accounts)
• Forgotten operating systems that may be weak points or exploited first within a network

Or when analyzing drivers on multiple hosts, an investigator could glance at a dashboard and determine any of the following and more:

• Number of drivers per host
• Largest driver
• Smallest driver
• Most common driver file name
• Most common driver path
• Least common driver file name
• Least common driver path

Conclusion

 These are just some examples of interesting data one might pull from analyzing many collections.  The possibilities are probably endless.  Let us know what you think.  Thanks.