Detection: Baseline of SMB Traffic - MLTK

REMOVED DETECTION

This detection has been removed from the Splunk Threat Research content library and is no longer maintained or supported.

Reason: All detection(s) which leverage this baseline have been deprecated. As such, this baseline has been deprecated as well.

Removed in version: 5.26.0

If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2026-05-13 ID: df98763b-0b08-4281-8ef9-08db7ac572a9 Author: Rico Valdez, Splunk Type: Baseline Product: Splunk Enterprise Security

Description

This search is used to build a Machine Learning Toolkit (MLTK) model to characterize the number of SMB connections observed each hour for every day of week. By default, the search uses the last 30 days of data to build the model. The model created by this search is then used in the corresponding detection search to identify outliers in the number of SMB connections for that hour and day of the week.

Search

1
2| tstats `security_content_summariesonly` count from datamodel=Network_Traffic where All_Traffic.dest_port=139 OR All_Traffic.dest_port=445 OR All_Traffic.app=smb by _time span=1h, All_Traffic.src 
3| eval HourOfDay=strftime(_time, "%H") 
4| eval DayOfWeek=strftime(_time, "%A") 
5| `drop_dm_object_name("All_Traffic")` 
6| fit DensityFunction count by "HourOfDay,DayOfWeek" into smb_pdfmodel

Data Source

No data sources specified for this detection.

Macros Used

Name	Value
security_content_summariesonly	`summariesonly=`summariesonly_config `allow_old_summaries=`oldsummaries_config `fillnull_value=`fillnull_config``

Annotations

- MITRE ATT&CK

+ Kill Chain Phases

+ NIST

+ CIS

- Threat Actors

ID	Technique	Tactic

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`N/A`
Earliest Time	`N/A`
Latest Time	`N/A`
Schedule Window	`N/A`
Creates Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	No

Implementation

You must be ingesting network traffic and populating the Network_Traffic data model. In addition, you must have the Machine Learning Toolkit (MLTK) version >= 4.2 installed, along with any required dependencies. To improve your results, you may consider adding "src" to the by clause, which will build the model for each unique source in your enviornment. However, if you have a large number of hosts in your environment, this search may be very resource intensive. In this case, you may need to raise the value of max_inputs and/or max_groups in the MLTK settings for the DensityFunction algorithm, then ensure that the search completes in a reasonable timeframe. By default, the search builds the model using the past 30 days of data. You can modify the search window to build the model over a longer period of time, which may give you better results. You may also want to periodically re-run this search to rebuild the model with the latest data. More information on the algorithm used in the search can be found at https://help.splunk.com/en/splunk-enterprise/apply-machine-learning/use-splunk-machine-learning-toolkit/5.5.0/algorithms-and-scoring-metrics-in-mltk/algorithms-in-the-splunk-machine-learning-toolkit#densityfunction-0.

Known False Positives

No false positives have been identified at this time.

Associated Analytic Story

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	Not Applicable	N/A	`N/A`	`N/A`
Integration	Not Applicable	N/A	`N/A`	`N/A`

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub |

Version: 3