:warning: THIS IS A EXPERIMENTAL DETECTION

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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Description

The following analytic identifies patterns within Okta data to determine the amount of successful and failed pushes. Based on that, eval statements determine a finding of whether this is suspicious or not. The events are within a window of time and may be tuned as needed.

  • Type: Hunting
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Authentication
  • Last Updated: 2022-09-27
  • Author: Michael Haag, Marissa Bower, Splunk
  • ID: 97e2fe57-3740-402c-988a-76b64ce04b8d

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1110 Brute Force Credential Access
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`okta` eventType=system.push.send_factor_verify_push OR ((legacyEventType=core.user.factor.attempt_success) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) OR ((legacyEventType=core.user.factor.attempt_fail) AND (debugContext.debugData.factor=OKTA_VERIFY_PUSH)) 
| stats count(eval(legacyEventType="core.user.factor.attempt_success")) as successes count(eval(legacyEventType="core.user.factor.attempt_fail")) as failures count(eval(eventType="system.push.send_factor_verify_push")) as pushes by authenticationContext.externalSessionId,user,_time 
| stats latest(_time) as lasttime earliest(_time) as firsttime sum(successes) as successes sum(failures) as failures sum(pushes) as pushes by authenticationContext.externalSessionId,user 
| eval seconds=lasttime-firsttime 
| eval lasttime=strftime(lasttime, "%c") 
| search (pushes>1) 
| eval totalattempts=successes+failures 
| eval finding="Normal authentication pattern" 
| eval finding=if(failures==pushes AND pushes>1,"Authentication attempts not successful because multiple pushes denied",finding) 
| eval finding=if(totalattempts==0,"Multiple pushes sent and ignored",finding) 
| eval finding=if(successes>0 AND pushes>3,"Probably should investigate. Multiple pushes sent, eventual successful authentication!",finding) 
| where seconds<600 AND finding="Multiple pushes sent, eventual successful authentication!" AND totalattempts > 5 
| sort - pushes, totalattempts 
| `okta_mfa_exhaustion_hunt_filter`

Macros

The SPL above uses the following Macros:

:information_source: okta_mfa_exhaustion_hunt_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
  • user
  • src_ip
  • eventType
  • status

How To Implement

This analytic is specific to Okta and requires Okta logs to be ingested.

Known False Positives

False positives may be present. Tune Okta and tune the analytic to ensure proper fidelity. Modify risk score as needed. Drop to anomaly until tuning is complete.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
18.0 30 60 $user$ account has rejected multiple Okta pushes.

: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.

Reference

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: 1