This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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This search uses the Machine Learning Toolkit (MLTK) to identify spikes in the number of Server Message Block (SMB) connections.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Traffic
  • Last Updated: 2020-07-22
  • Author: Rico Valdez, Splunk
  • ID: d25773ba-9ad8-48d1-858e-07ad0bbeb828




ID Technique Tactic
T1021.002 SMB/Windows Admin Shares Lateral Movement
T1021 Remote Services Lateral Movement
Kill Chain Phase
  • Exploitation
  • DE.AE
  • CIS 13
| tstats `security_content_summariesonly` count values(All_Traffic.dest_ip) as dest values(All_Traffic.dest_port) as port 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 
| eval HourOfDay=strftime(_time, "%H") 
| eval DayOfWeek=strftime(_time, "%A") 
| `drop_dm_object_name(All_Traffic)` 
| apply smb_pdfmodel threshold=0.001 
| rename "IsOutlier(count)" as isOutlier 
| search isOutlier > 0 
| sort -count 
| table _time src dest port count 
| `smb_traffic_spike___mltk_filter` 


The SPL above uses the following Macros:

:information_source: smb_traffic_spike_-_mltk_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • _time
  • All_Traffic.dest_ip
  • All_Traffic.dest_port
  • All_Traffic.app
  • All_Traffic.src

How To Implement

To successfully implement this search, you will need to ensure that DNS data is populating the Network_Traffic data model. In addition, the latest version of Machine Learning Toolkit (MLTK) must be installed on your search heads, along with any required dependencies. Finally, the support search "Baseline of SMB Traffic - MLTK" must be executed before this detection search, because it builds a machine-learning (ML) model over the historical data used by this search. It is important that this search is run in the same app context as the associated support search, so that the model created by the support search is available for use. You should periodically re-run the support search to rebuild the model with the latest data available in your environment.
This search produces a field (Number of events,count) that are not yet supported by ES Incident Review and therefore cannot be viewed when a notable event is raised. This field contributes additional context to the notable. To see the additional metadata, add the following field, if not already present, to Incident Review - Event Attributes (Configure > Incident Management > Incident Review Settings > Add New Entry): \

  1. Label: Number of events, Field: count
    Detailed documentation on how to create a new field within Incident Review is found here: https://docs.splunk.com/Documentation/ES/5.3.0/Admin/Customizenotables#Add_a_field_to_the_notable_event_details

    Known False Positives

    If you are seeing more results than desired, you may consider reducing the value of the threshold in the search. You should also periodically re-run the support search to re-build the ML model on the latest data. Please update the smb_traffic_spike_mltk_filter macro to filter out false positive results

Associated Analytic Story


Risk Score Impact Confidence Message
25.0 50 50 tbd

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.


Test Dataset

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

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