:no_entry: THIS IS A DEPRECATED DETECTION

This detection has been marked deprecated by the Splunk Threat Research team. This means that it will no longer be maintained or supported.

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Description

This search is used to identify user accounts that share a common password.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2018-10-08
  • Author: Jim Apger, Splunk
  • ID: 31337a1a-53b9-4e05-96e9-55c934cb71d3

Annotations

ATT&CK
Kill Chain Phase
NIST
  • DE.AE
CIS20
  • CIS 10
CVE 


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`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  
| rex field=form_data "login\[username\]=(?<Username>[^&
|^$]+)" 
| rex field=form_data "login\[password\]=(?<Password>[^&
|^$]+)" 
| stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password
|where UniqueUsernames>5 
| `web_fraud___password_sharing_across_accounts_filter`

Macros

The SPL above uses the following Macros:

:information_source: web_fraud_-_password_sharing_across_accounts_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
  • http_content_type
  • uri

How To Implement

We need to start with a dataset that allows us to see the values of usernames and passwords that users are submitting to the website hosting the Magento2 e-commerce platform (commonly found in the HTTP form_data field). A tokenized or hashed value of a password is acceptable and certainly preferable to a clear-text password. Common data sources used for this detection are customized Apache logs, customized IIS, and Splunk Stream.

Known False Positives

As is common with many fraud-related searches, we are usually looking to attribute risk or synthesize relevant context with loosely written detections that simply detect anamoluous behavior.

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

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