ID | Technique | Tactic |
---|---|---|
T1078 | Valid Accounts | Defense Evasion |
Detection: Web Fraud - Anomalous User Clickspeed
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.
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 | search * |
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
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 |
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
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
tbd | 25 | 50 | 50 |
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