| ID | Technique | Tactic |
|---|---|---|
| T1649 | Steal or Forge Authentication Certificates | Credential Access |
Detection: Steal or Forge Authentication Certificates Behavior Identified
Description
The following analytic identifies potential threats related to the theft or forgery of authentication certificates. It detects when five or more analytics from the Windows Certificate Services story trigger within a specified timeframe. This detection leverages aggregated risk scores and event counts from the Risk data model. This activity is significant as it may indicate an ongoing attack aimed at compromising authentication mechanisms. If confirmed malicious, attackers could gain unauthorized access to sensitive systems and data, potentially leading to severe security breaches.
Search
1
2| tstats `security_content_summariesonly` min(_time) as firstTime max(_time) as lastTime sum(All_Risk.calculated_risk_score) as risk_score, count(All_Risk.calculated_risk_score) as risk_event_count, values(All_Risk.annotations.mitre_attack.mitre_tactic_id) as annotations.mitre_attack.mitre_tactic_id, dc(All_Risk.annotations.mitre_attack.mitre_tactic_id) as mitre_tactic_id_count, values(All_Risk.annotations.mitre_attack.mitre_technique_id) as annotations.mitre_attack.mitre_technique_id, dc(All_Risk.annotations.mitre_attack.mitre_technique_id) as mitre_technique_id_count, values(All_Risk.tag) as tag, values(source) as source, dc(source) as source_count from datamodel=Risk.All_Risk where All_Risk.analyticstories="Windows Certificate Services" All_Risk.risk_object_type="system" by All_Risk.risk_object All_Risk.risk_object_type All_Risk.annotations.mitre_attack.mitre_tactic
3| `drop_dm_object_name(All_Risk)`
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| where source_count >= 5
7| `steal_or_forge_authentication_certificates_behavior_identified_filter`
Data Source
No data sources specified for this detection.
Macros Used
| Name | Value |
|---|---|
| security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
| steal_or_forge_authentication_certificates_behavior_identified_filter | search * |
steal_or_forge_authentication_certificates_behavior_identified_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
KillChainPhase.EXPLOITAITON
NistCategory.DE_AE
Cis18Value.CIS_10
APT29
Default Configuration
This detection is configured by default in Splunk Enterprise Security to run with the following settings:
| Setting | Value |
|---|---|
| Disabled | true |
| Cron Schedule | 0 * * * * |
| Earliest Time | -70m@m |
| Latest Time | -10m@m |
| Schedule Window | auto |
| Creates Notable | Yes |
| Rule Title | %name% |
| Rule Description | %description% |
| Notable Event Fields | user, dest |
| Creates Risk Event | False |
This configuration file applies to all detections of type Correlation. These correlations will generate Notable Events.
Implementation
The Windows Certificate Services analytic story must have 5 or more analytics enabled. In addition, ensure data is being logged that is required. Modify the correlation as needed based on volume of noise related to the other analytics.
Known False Positives
False positives may be present based on automated tooling or system administrators. Filter as needed.
Associated Analytic Story
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| Steal or Forge Authentication Certificates Behavior Identified on $risk_object$. | 72 | 80 | 90 |
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
References
Detection Testing
| Test Type | Status | Dataset | Source | Sourcetype |
|---|---|---|---|---|
| Validation | ✅ Passing | N/A | N/A | N/A |
| Unit | ✅ Passing | Dataset | certs |
stash |
| Integration | ✅ Passing | Dataset | certs |
stash |
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: GitHub | Version: 3