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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The following hunting analytic leverages Event ID 4769, A Kerberos service ticket was requested, to identify an unusual number of computer service ticket requests from one source. When a domain joined endpoint connects to a remote endpoint, it first will request a Kerberos Ticket with the computer name as the Service Name. An endpoint requesting a large number of computer service tickets for different endpoints could represent malicious behavior like lateral movement, malware staging, reconnaissance, etc.
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number of service requests. To customize this analytic, users can try different combinations of the bucket span time, the calculation of the upperBound field as well as the Outlier calculation. This logic can be used for real time security monitoring as well as threat hunting exercises.\

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2021-12-01
  • Author: Mauricio Velazco, Splunk
  • ID: ac3b81c0-52f4-11ec-ac44-acde48001122




ID Technique Tactic
T1078 Valid Accounts Defense Evasion, Persistence, Privilege Escalation, Initial Access
Kill Chain Phase
  • Exploitation
  • Installation
  • Delivery
  • DE.AE
  • CIS 10
 `wineventlog_security` EventCode=4769 Service_Name="*$" Account_Name!="*$*" 
| bucket span=2m _time 
| stats dc(Service_Name) AS unique_targets values(Service_Name) as host_targets by _time, Client_Address, Account_Name 
| eventstats avg(unique_targets) as comp_avg , stdev(unique_targets) as comp_std by Client_Address, Account_Name 
| eval upperBound=(comp_avg+comp_std*3) 
| eval isOutlier=if(unique_targets >10 and unique_targets >= upperBound, 1, 0) 
| `unusual_number_of_computer_service_tickets_requested_filter`


The SPL above uses the following Macros:

:information_source: unusual_number_of_computer_service_tickets_requested_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
  • EventCode
  • Ticket_Options
  • Ticket_Encryption_Type
  • dest
  • service
  • service_id

How To Implement

To successfully implement this search, you need to be ingesting Domain Controller and Kerberos events. The Advanced Security Audit policy setting Audit Kerberos Authentication Service within Account Logon needs to be enabled.

Known False Positives

An single endpoint requesting a large number of computer service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systeams and missconfigured systems.

Associated Analytic Story


Risk Score Impact Confidence Message
42.0 70 60  

: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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