Windows Abused Web Services
Description
The following analytic detects a suspicious process making DNS queries to known, abused web services such as text-paste sites, VoIP, secure tunneling, instant messaging, and digital distribution platforms. This detection leverages Sysmon logs with Event ID 22, focusing on specific query names. This activity is significant as it may indicate an adversary attempting to download malicious files, a common initial access technique. If confirmed malicious, this could lead to unauthorized code execution, data exfiltration, or further compromise of the target host.
- Type: TTP
-
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2024-05-22
- Author: Teoderick Contreras, Splunk
- ID: 01f0aef4-8591-4daa-a53d-0ed49823b681
Annotations
Kill Chain Phase
- Command and Control
NIST
- DE.CM
CIS20
- CIS 10
CVE
Search
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`sysmon` EventCode=22 QueryName IN ("*pastebin*",""*textbin*"", "*ngrok.io*", "*discord*", "*duckdns.org*", "*pasteio.com*")
| stats count min(_time) as firstTime max(_time) as lastTime by Image QueryName QueryStatus process_name QueryResults Computer
| rename Computer as dest
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `windows_abused_web_services_filter`
Macros
The SPL above uses the following Macros:
windows_abused_web_services_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
- Image
- QueryName
- QueryStatus
- process_name
- QueryResults
- Computer
How To Implement
This detection relies on sysmon logs with the Event ID 22, DNS Query. We suggest you run this detection at least once a day over the last 14 days.
Known False Positives
Noise and false positive can be seen if the following instant messaging is allowed to use within corporate network. In this case, a filter is needed.
Associated Analytic Story
RBA
Risk Score | Impact | Confidence | Message |
---|---|---|---|
36.0 | 60 | 60 | a network connection on known abused web services from $dest$ |
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: 2