Detection rules › Splunk

Unusual Number of Kerberos Service Tickets Requested

Author
Mauricio Velazco, Dean Luxton, Splunk
Source
upstream

The following analytic identifies an unusual number of Kerberos service ticket requests, potentially indicating a kerberoasting attack. It leverages Kerberos Event 4769 and calculates the standard deviation for each host, using the 3-sigma rule to detect anomalies. This activity is significant as kerberoasting allows adversaries to request service tickets and crack them offline, potentially gaining privileged access to the domain. If confirmed malicious, this could lead to unauthorized access to sensitive accounts and escalation of privileges within the Active Directory environment.

MITRE ATT&CK coverage

TacticTechniques
Credential AccessT1558.003 Steal or Forge Kerberos Tickets: Kerberoasting

Event coverage

ProviderEvent IDTitle
Security-Auditing4769A Kerberos service ticket was requested.

Stages and Predicates

Stage 1: search

search EventCode=4769 ServiceName!="*$" TicketEncryptionType=0x17

Stage 2: bucket

bucket span=2m _time

Stage 3: stats

stats dc(ServiceName) AS unique_services,AS requested_services,AS user_category,AS src_category,AS dest BY _time, user, src

Stage 4: eventstats

eventstats avg(unique_services) AS comp_avg BY user, src

Stage 5: eval

eval ... using (comp_avg, comp_std)

Stage 6: eval

eval ... using (unique_services, upperBound)

Stage 7: search

search isOutlier=1

Stage 8: search

search `macro`

Indicators

Each row is a field, operator, and value that the rule matches. The corpus column counts how many other rules in the catalog look for the same combination: high numbers point to widely-used, community-vetted indicators. Blank or 1 shows that the indicator is specific to this rule.

FieldKindValues
EventCodeeq
  • 4769 corpus 6 (splunk 6)
ServiceNamene
  • "*$" corpus 2 (splunk 2)
TicketEncryptionTypeeq
  • 0x17 corpus 7 (splunk 4, sigma 3)
isOutliereq
  • 1 corpus 16 (splunk 16)

Neighbors

Stricter alternatives (narrower than this rule)

The rules below may be useful if you find the current rule is too noisy / lacks specificity.

Broader alternatives (more inclusive than this rule)

These rules match a superset of what this rule catches. They cover the same events plus more. Use them if you want wider coverage and can absorb more false positives.