:warning: THIS IS A EXPERIMENTAL DETECTION

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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Description

The following analytic detects spikes in Server Message Block (SMB) traffic connections, which are used for sharing files and resources between computers. It leverages network traffic logs to monitor connections on ports 139 and 445, and SMB application usage. By calculating the average and standard deviation of SMB connections over the past 70 minutes, it identifies sources exceeding two standard deviations from the average. This activity is significant as it may indicate potential SMB-based attacks, such as ransomware or data theft. If confirmed malicious, attackers could exfiltrate data or spread malware within the network.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Network_Traffic
  • Last Updated: 2024-05-27
  • Author: David Dorsey, Splunk
  • ID: 7f5fb3e1-4209-4914-90db-0ec21b936378

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1021.002 SMB/Windows Admin Shares Lateral Movement
T1021 Remote Services Lateral Movement
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| 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 
| `drop_dm_object_name("All_Traffic")` 
| eventstats max(_time) as maxtime 
| stats count as num_data_samples max(eval(if(_time >= relative_time(maxtime, "-70m@m"), count, null))) as count avg(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as avg stdev(eval(if(_time<relative_time(maxtime, "-70m@m"), count, null))) as stdev by src 
| eval upperBound=(avg+stdev*2), isOutlier=if(count > upperBound AND num_data_samples >=50, 1, 0) 
| where isOutlier=1 
| table src count 
| `smb_traffic_spike_filter`

Macros

The SPL above uses the following Macros:

:information_source: smb_traffic_spike_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_port
  • All_Traffic.app
  • All_Traffic.src

How To Implement

This search requires you to be ingesting your network traffic logs and populating the Network_Traffic data model.

Known False Positives

A file server may experience high-demand loads that could cause this analytic to trigger.

Associated Analytic Story

RBA

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.

Reference

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

source | version: 4