HuntLib

A Python library to help with some common threat hunting data analysis operations

What's Here?

The huntlib module provides two major object classes as well as a few convenience functions.

ElasticDF: Search Elastic and return results as a Pandas DataFrame
SplunkDF: Search Splunk and return results as a Pandas DataFrame
entropy() / entropy_per_byte(): Calculate Shannon entropy
promptCreds(): Prompt for login credentials in the terminal or from within a Jupyter notebook.
edit_distance(): Calculate how "different" two strings are from each other

huntlib.elastic.ElasticDF

The ElasticDF() class searches Elastic and returns results as a Pandas DataFrame. This makes it easier to work with the search results using standard data analysis techniques.

Example usage:

Create a plaintext connection to the Elastic server, no authentication

e = ElasticDF(
                url="http://localhost:9200"
)

The same, but with SSL and authentication

e = ElasticDF(
                url="https://localhost:9200",
                ssl=True,
                username="myuser",
                password="mypass"
)

Fetch search results from an index or index pattern for the previous day

df = e.search_df(
                  lucene="item:5282 AND color:red",
                  index="myindex-*",
                  days=1
)

The same, but do not flatten structures into individual columns. This will result in each structure having a single column with a JSON string describing the structure.

df = e.search_df(
                  lucene="item:5282 AND color:red",
                  index="myindex-*",
                  days=1,
                  normalize=False
)

A more complex example, showing how to set the Elastic document type, use Python-style datetime objects to constrain the search to a certain time period, and a user-defined field against which to do the time comparisons. The result size will be limited to no more than 1500 entries.

df = e.search_df(
                  lucene="item:5285 AND color:red",
                  index="myindex-*",
                  doctype="doc", date_field="mydate",
                  start_time=datetime.now() - timedelta(days=8),
                  end_time=datetime.now() - timedelta(days=6),
                  limit=1500
)

The search and search_df methods will raise InvalidRequestSearchException in the event that the search request is syntactically correct but is otherwise invalid. For example, if you request more results be returned than the server is able to provide. They will raise AuthenticationErrorSearchException in the event the server denied the credentials during login. They can also raise an UnknownSearchException for other situations, in which case the exception message will contain the original error message returned by Elastic so you can figure out what went wrong.

huntlib.splunk.SplunkDF

The SplunkDF class search Splunk and returns the results as a Pandas DataFrame. This makes it easier to work with the search results using standard data analysis techniques.

Example Usage

Establish an connection to the Splunk server. Whether this is SSL/TLS or not depends on the server, and you don't really get a say.

s = SplunkDF(
              host=splunk_server,
              username="myuser",
              password="mypass"
)

Fetch all search results across all time

df = s.search_df(
                  spl="search index=win_events EventCode=4688"
)

Fetch only specific fields, still across all time

df = s.search_df(
                  spl="search index=win_events EventCode=4688 | table ComputerName _time New_Process_Name Account_Name Creator_Process_ID New_Process_ID Process_Command_Line"
)

Time bounded search, 2 days prior to now

df = s.search_df(
                  spl="search index=win_events EventCode=4688",
                  days=2
)

Time bounded search using Python datetime() values

df = s.search_df(
                  spl="search index=win_events EventCode=4688",
                  start_time=datetime.now() - timedelta(days=2),
                  end_time=datetime.now()
)

Time bounded search using Splunk notation

df = s.search_df(
                  spl="search index=win_events EventCode=4688",
                  start_time="-2d@d",
                  end_time="@d"
)

Limit the number of results returned to no more than 1500

df = s.search_df(
                  spl="search index=win_events EventCode=4688",
                  limit=1500
)

NOTE: The value specified as the limit is also subject to a server-side max value. By default, this is 50000 and can be changed by editing limits.conf on the Splunk server. If you use the limit parameter, the number of search results you receive will be the lesser of the following values: 1) the actual number of results available, 2) the number you asked for with limit, 3) the server-side maximum result size. If you omit limit altogether, you will get the true number of search results available without subject to additional limits, though your search may take much longer to complete.

SplunkDF will raise AuthenticationErrorSearchException during initialization in the event the server denied the supplied credentials.

Miscellaneous Functions

Entropy

We define two entropy functions, entropy() and entropy_per_byte(). Both accept a single string as a parameter. The entropy() function calculates the Shannon entropy of the given string, while entropy_per_byte() attempts to normalize across strings of various lengths by returning the Shannon entropy divided by the length of the string. Both return values are float.

>>> entropy("The quick brown fox jumped over the lazy dog.")
4.425186429663008
>>> entropy_per_byte("The quick brown fox jumped over the lazy dog.")
0.09833747621473352

The higher the value, the more data potentially embedded in it.

Credential Handling

Sometimes you need to provide credentials for a service, but don't want to hard-code them into your scripts, especially if you're collaborating on a hunt. huntlib provides the promptCreds() function to help with this. This function works well both in the terminal and when called from within a Jupyter notebook.

Call it like so:

(username, password) = promptCreds()

You can change one or both of the username/password prompts by passing arguments:

(username, password) = promptCreds(uprompt="LAN ID: ",
                                   pprompt="LAN Pass: ")

String Similarity

String similarity can be expressed in terms of "edit distance", or the number of single-character edits necessary to turn the first string into the second string. This is often useful when, for example, you want to find two strings that very similar but not identical (such as when hunting for process impersonation).

There are a number of different ways to compute similarity. huntlib provides the edit_distance() function for this, which supports several algorithms:

Here's an example:

>>> huntlib.edit_distance('svchost', 'scvhost')
1

You can specify a different algorithm using the method parameter. Valid methods are levenshtein, damerau-levenshtein, hamming, jaro and jaro-winkler. The default is damerau-levenshtein.

>>> huntlib.edit_distance('svchost', 'scvhost', method='levenshtein')
2

halilozturkci/huntlib