: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.

Try in Splunk Security Cloud

Description

The search queries the authentication logs for assets that are categorized as routers in the ES Assets and Identity Framework, to identify connections that have not been seen before in the last 30 days.

  • Type: TTP
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
  • Datamodel: Authentication
  • Last Updated: 2017-09-12
  • Author: Bhavin Patel, Splunk
  • ID: bce3ed7c-9b1f-42a0-abdf-d8b123a34836

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.CM
CIS20
  • CIS 13
CVE 


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| tstats `security_content_summariesonly` count earliest(_time) as earliest latest(_time) as latest from datamodel=Authentication where Authentication.dest_category=router by Authentication.dest Authentication.user
| eval isOutlier=if(earliest >= relative_time(now(), "-30d@d"), 1, 0) 
| where isOutlier=1
| `security_content_ctime(earliest)`
| `security_content_ctime(latest)` 
| `drop_dm_object_name("Authentication")` 
| `detect_new_login_attempts_to_routers_filter`

Macros

The SPL above uses the following Macros:

:information_source: detect_new_login_attempts_to_routers_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
  • Authentication.dest_category
  • Authentication.dest
  • Authentication.user

How To Implement

To successfully implement this search, you must ensure the network router devices are categorized as "router" in the Assets and identity table. You must also populate the Authentication data model with logs related to users authenticating to routing infrastructure.

Known False Positives

Legitimate router connections may appear as new connections

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 tbd

: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