splunk-security_content/docs/_posts/2020-10-21-detect_kerberoasting.md

title, excerpt, categories, last_modified_at, toc, toc_label, tags

title	excerpt	categories	last_modified_at	toc	toc_label	tags
Detect Kerberoasting	Kerberoasting, Steal or Forge Kerberos Tickets	Endpoint	2020-10-21	true		Kerberoasting Credential Access Steal or Forge Kerberos Tickets Credential Access Splunk Behavioral Analytics Certificates

⚠️ 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 search detects a potential kerberoasting attack via service principal name requests

• Type: TTP
• Product: Splunk Behavioral Analytics
• Datamodel: Certificates
• Last Updated: 2020-10-21
• Author: Xiao Lin, Splunk
• ID: dabdd6d7-3e10-42be-8711-4e124f7a3850

ATT&CK

ID	Technique	Tactic
T1558.003	Kerberoasting	Credential Access

| T1558 | Steal or Forge Kerberos Tickets | Credential Access |

Search

 
| from read_ssa_enriched_events() 
| eval _time=map_get(input_event, "_time"), EventCode=map_get(input_event, "event_code"), TicketOptions=map_get(input_event, "ticket_options"), TicketEncryptionType=map_get(input_event, "ticket_encryption_type"), ServiceName=map_get(input_event, "service_name"), ServiceID=map_get(input_event, "service_id"), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null), event_id=ucast(map_get(input_event, "event_id"), "string", null) 
| where EventCode="4769" AND TicketOptions="0x40810000" AND TicketEncryptionType="0x17" 
| first_time_event input_columns=["EventCode","TicketOptions","TicketEncryptionType","ServiceName","ServiceID"] 
| where first_time_EventCode_TicketOptions_TicketEncryptionType_ServiceName_ServiceID 
| eval start_time=_time, end_time=_time 
| eval body=create_map(["event_id", event_id, "EventCode", EventCode, "ServiceName", ServiceName, "TicketOptions", TicketOptions, "TicketEncryptionType", TicketEncryptionType]), entities = mvappend(ucast(map_get(input_event, "dest_user_id"), "string", null), ucast(map_get(input_event, "dest_device_id"), "string", null)) 
| select start_time, end_time, entities, body 
| into write_ssa_detected_events();

Macros

The SPL above uses the following Macros:

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

Required field

• service_name
• _time
• event_code
• ticket_encryption_type
• service_id
• ticket_options

How To Implement

The test data is converted from Windows Security Event logs generated from Attach Range simulation and used in SPL search and extended to SPL2

Known False Positives

Older systems that support kerberos RC4 by default NetApp may generate false positives

Associated Analytic story

• Credential Dumping

Kill Chain Phase

• Actions on Objectives

RBA

Risk Score	Impact	Confidence	Message
14.0	70	20	Kerberoasting malware is potentially applying stolen credentials. Operation is performed at the device dest_device_id, by the account dest_user_id via command cmd_line

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

Reference

• [Initial ESCU implementation by Jose Hernandez and Patrick Bareiss](Initial ESCU implementation by Jose Hernandez and Patrick Bareiss)

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: 2