⚠️ WARNING THIS IS A EXPERIMENTAL DETECTION

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

This is an anomaly generating detection looking for multiple interactive logins within a specific time period. An insider threat may attempt to steal colleagues credentials in low tech, undetectable methods, in order to gain access to additional information or to hide their own behavior. This should capture their attempted use of those credentials on a workstation.

  • Type: Anomaly
  • Product: Splunk Behavioral Analytics
  • Datamodel: Endpoint_Processes
  • Last Updated: 2021-12-07
  • Author: Lou Stella, Splunk
  • ID: 629cbf9e-5785-11ec-9611-acde48001122

ATT&CK

ID Technique Tactic
T1078.002 Domain Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
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2
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10
11
| from read_ssa_enriched_events() 
| eval device=ucast(map_get(input_event, "dest_device_id"), "string", null), auth_type=ucast(map_get(input_event, "authentication_type"), "string", null), timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)), src_user=ucast(map_get(input_event, "dest_user_original_artifact"), "string", null), signature_id=ucast(map_get(input_event, "EventCode"), "string", null) 
| where signature_id="4624" 
| where auth_type="2" OR auth_type="11" 
| where NOT (src_user="SYSTEM") AND NOT (src_user="ANONYMOUS LOGON") 
| stats estdc(src_user) AS user_counter by device, span(timestamp, 600s, 300s) 
| where user_counter>=2  
| rename window_end AS timestamp 
| eval start_time=window_start, end_time=timestamp, entities=mvappend(device), body=create_map(["user_counter", user_counter, "device", device]) 
| into write_ssa_detected_events();

Macros

The SPL above uses the following Macros:

Note that anomalous_usage_of_account_credentials_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required field

  • _time

How To Implement

To successfully implement this detection, you need to be ingesting logon events from workstations.

Known False Positives

Shared workstations can cause false positives

Associated Analytic story

Kill Chain Phase

  • Privilege Escalation
  • Lateral Movement

RBA

Risk Score Impact Confidence Message
6.0 20 30 Multiple interactive logins detected on $device$

Note that risk score is calculated base on the following formula: (Impact * Confidence)/100

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

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