The following hunting analytic leverages Kerberos Event 4769, A Kerberos service ticket was requested, to identify a potential kerberoasting attack against Active Directory networks. Kerberoasting allows an adversary to request kerberos tickets for domain accounts typically used as service accounts and attempt to crack them offline allowing them to obtain privileged access to the domain.
The detection calculates the standard deviation for each host and leverages the 3-sigma statistical rule to identify an unusual number service ticket requests. To customize this analytic, users can try different combinations of the
bucket span time and the calculation of the
- Type: Anomaly
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2022-02-08
- Author: Mauricio Velazco, Splunk
- ID: eb3e6702-8936-11ec-98fe-acde48001122
Kill Chain Phase
1 2 3 4 5 6 7 8 `wineventlog_security` EventCode=4769 Service_Name!="*$" Ticket_Encryption_Type=0x17 | bucket span=2m _time | stats dc(Service_Name) AS unique_services values(Service_Name) as requested_services by _time, Client_Address | eventstats avg(unique_services) as comp_avg , stdev(unique_services) as comp_std by Client_Address | eval upperBound=(comp_avg+comp_std*3) | eval isOutlier=if(unique_services > 2 and unique_services >= upperBound, 1, 0) | search isOutlier=1 | `unusual_number_of_kerberos_service_tickets_requested_filter`
The SPL above uses the following Macros:
unusual_number_of_kerberos_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.
List of fields required to use this analytic.
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 kerberos service tickets is not common behavior. Possible false positive scenarios include but are not limited to vulnerability scanners, administration systems and missconfigured systems.
Associated Analytic Story
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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