Okta User Logins From Multiple Cities
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.
Description
This search detects logins from the same user from different cities in a 24 hour period.
- Type: Anomaly
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2020-07-21
- Author: Rico Valdez, Splunk
- ID: 7594fa07-9f34-4d01-81cc-d6af6a5db9e8
Annotations
ATT&CK
Kill Chain Phase
- Exploitation
- Installation
- Delivery
NIST
- DE.AE
CIS20
- CIS 10
CVE
Search
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`okta` displayMessage="User login to Okta" client.geographicalContext.city!=null
| stats min(_time) as firstTime max(_time) as lastTime dc(client.geographicalContext.city) as locations values(client.geographicalContext.city) as cities values(client.geographicalContext.state) as states by user
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `okta_user_logins_from_multiple_cities_filter`
| search locations > 1
Macros
The SPL above uses the following Macros:
okta_user_logins_from_multiple_cities_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
- displayMessage
- client.geographicalContext.city
- client.geographicalContext.state
- user
How To Implement
This search is specific to Okta and requires Okta logs are being ingested in your Splunk deployment.
Known False Positives
Users in your enviornment may legitmately be travelling and loggin in from different locations. This search is useful for those users that should not be travelling for some reason, such as the COVID-19 pandemic. The search also relies on the geographical information being populated in the Okta logs. It is also possible that a connection from another region may be attributed to a login from a remote VPN endpoint.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
25.0 | 50 | 50 | tbd |
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: 2