Detection: Web Fraud - Password Sharing Across Accounts

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.

Updated Date: 2024-10-17 ID: 31337a1a-53b9-4e05-96e9-55c934cb71d3 Author: Jim Apger, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

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

Search

1`stream_http` http_content_type=text* uri=/magento2/customer/account/loginPost*  
2| rex field=form_data "login\[username\]=(?<Username>[^&
3|^$]+)" 
4| rex field=form_data "login\[password\]=(?<Password>[^&
5|^$]+)" 
6| stats dc(Username) as UniqueUsernames values(Username) as user list(src_ip) as src_ip by Password
7|where UniqueUsernames>5 
8| `web_fraud___password_sharing_across_accounts_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name	Value
stream_http	`sourcetype=stream:http`
web_fraud___password_sharing_across_accounts_filter	`search *`

web_fraud___password_sharing_across_accounts_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

NistCategory.DE_AE

Cis18Value.CIS_10

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 Risk Event	True

This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Web Fraud Detection

Risk Based Analytics (RBA)

Risk Message	Risk Score	Impact	Confidence
tbd	25	50	50

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	❌ Failing	N/A	`N/A`	`N/A`
Integration	❌ Failing	N/A	`N/A`	`N/A`

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: 3