Detection: Web Fraud - Anomalous User Clickspeed

REMOVED DETECTION

This detection has been removed from the Splunk Threat Research content library and is no longer maintained or supported.

Reason: Detection deprecated as it no longer effectively identifies the intended malicious activity

Removed in version: 5.2.0

If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2026-05-13 ID: 31337bbb-bc22-4752-b599-ef192df2dc7a Author: Jim Apger, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

This search is used to examine web sessions to identify those where the clicks are occurring too quickly for a human or are occurring with a near-perfect cadence (high periodicity or low standard deviation), resembling a script driven session.

Search

1`stream_http` http_content_type=text* 
2| rex field=cookie "form_key=(?<session_id>\w+)" 
3| streamstats window=2 current=1 range(_time) as TimeDelta by session_id 
4| where TimeDelta>0 
5|stats count stdev(TimeDelta) as ClickSpeedStdDev avg(TimeDelta) as ClickSpeedAvg by session_id 
6| where count>5 AND (ClickSpeedStdDev<.5 OR ClickSpeedAvg<.5) 
7| `web_fraud___anomalous_user_clickspeed_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name	Value
stream_http	`sourcetype=stream:http`
web_fraud___anomalous_user_clickspeed_filter	``

web_fraud___anomalous_user_clickspeed_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
T1078	Valid Accounts	Initial Access

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting	Value
Disabled	true
Cron Schedule	`N/A`
Earliest Time	`N/A`
Latest Time	`N/A`
Schedule Window	`N/A`
Creates Finding (Notable)	No
Creates Intermediate Finding (Risk Event)	No

Anomaly detections generate Intermediate Findings (Risk Events). They do not generate a Finding (Notable) directly.

Implementation

Start with a dataset that allows you to see clickstream data for each user click on the website. That data must have a time stamp and must contain a reference to the session identifier being used by the website. This ties the clicks together into clickstreams. This value is usually found in the http cookie. With a bit of tuning, a version of this search could be used in high-volume scenarios, such as scraping, crawling, application DDOS, credit-card testing, account takeover, etc. 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 loosly written detections that simply detect anamoluous behavior.

Associated Analytic Story

Web Fraud Detection

References

Detection Testing

Test Type	Status	Dataset	Source	Sourcetype
Validation	Not Applicable	N/A	N/A	N/A
Unit	Not Applicable	N/A	`N/A`	`N/A`
Integration	Not Applicable	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: 4