Detection: Multiple Okta Users With Invalid Credentials From The Same IP

DEPRECATED DETECTION

This detection has been marked as deprecated by the Splunk Threat Research team. This means that it will no longer be maintained or supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Description

DEPRECATION NOTE - This search has been deprecated and replaced with Okta Multiple Users Failing To Authenticate From Ip. This analytic identifies multiple failed logon attempts from a single IP in a short period of time. Use this analytic to identify patterns of suspicious logins from a single source and filter as needed or use this to drive tuning for higher fidelity analytics.

1`okta` eventType=user.session.start outcome.result=FAILURE 
2| rename client.geographicalContext.country as country, client.geographicalContext.state as state, client.geographicalContext.city as city 
3| stats min(_time) as firstTime max(_time) as lastTime dc(src_user) as distinct_users values(src_user) as users by src_ip, displayMessage, outcome.reason, country, state, city 
4| `security_content_ctime(firstTime)` 
5| `security_content_ctime(lastTime)` 
6| search distinct_users > 5
7| `multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
okta eventtype=okta_log OR sourcetype = "OktaIM2:log"
multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter search *
multiple_okta_users_with_invalid_credentials_from_the_same_ip_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
ID Technique Tactic
T1110.003 Password Spraying Credential Access
T1078 Valid Accounts Defense Evasion
T1078.001 Default Accounts Initial Access
KillChainPhase.DELIVERY
KillChainPhase.EXPLOITAITON
KillChainPhase.INSTALLATION
NistCategory.DE_CM
Cis18Value.CIS_10
APT28
APT29
APT33
Agrius
Chimera
Ember Bear
HEXANE
Lazarus Group
Leafminer
Silent Librarian
APT18
APT28
APT29
APT33
APT39
APT41
Akira
Axiom
Carbanak
Chimera
Cinnamon Tempest
Dragonfly
FIN10
FIN4
FIN5
FIN6
FIN7
FIN8
Fox Kitten
GALLIUM
INC Ransom
Indrik Spider
Ke3chang
LAPSUS$
Lazarus Group
Leviathan
OilRig
POLONIUM
PittyTiger
Play
Sandworm Team
Silence
Silent Librarian
Star Blizzard
Suckfly
Threat Group-3390
Volt Typhoon
Wizard Spider
menuPass
Ember Bear
FIN13
Magic Hound

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 True
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.

Implementation

This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.

Known False Positives

A single public IP address servicing multiple legitmate users may trigger this search. In addition, the threshold of 5 distinct users may be too low for your needs. You may modify the included filter macro multiple_okta_users_with_invalid_credentials_from_the_same_ip_filter to raise the threshold or except specific IP adresses from triggering this search.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Multple user accounts have failed to authenticate from a single IP. 9 30 30
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 Not Applicable N/A N/A N/A
Unit Passing Dataset Okta OktaIM2:log
Integration ✅ Passing Dataset Okta OktaIM2:log

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: 4