ID | Technique | Tactic |
---|---|---|
T1497 | Virtualization/Sandbox Evasion | Defense Evasion |
T1497.003 | Time Based Evasion | Discovery |
Detection: Windows Time Based Evasion
Description
The following analytic detects potentially malicious processes that initiate a ping delay using an invalid IP address. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on command-line executions involving "ping 0 -n". This behavior is significant as it is commonly used by malware like NJRAT to introduce time delays for evasion tactics, such as delaying self-deletion. If confirmed malicious, this activity could indicate an active infection attempting to evade detection, potentially leading to further compromise and persistence within the environment.
Search
1
2| tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_name = "ping.exe" Processes.parent_process = "* ping 0 -n *" OR Processes.process = "* ping 0 -n *" by Processes.parent_process Processes.process_name Processes.process_id Processes.process_guid Processes.process Processes.user Processes.dest
3| `drop_dm_object_name("Processes")`
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `windows_time_based_evasion_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
CrowdStrike ProcessRollup2 | N/A | 'crowdstrike:events:sensor' |
'crowdstrike' |
N/A |
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
windows_time_based_evasion_filter | search * |
windows_time_based_evasion_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 Notable | Yes |
Rule Title | %name% |
Rule Description | %description% |
Notable Event Fields | user, dest |
Creates Risk Event | True |
Implementation
The detection is based on data that originates from Endpoint Detection and Response (EDR) agents. These agents are designed to provide security-related telemetry from the endpoints where the agent is installed. To implement this search, you must ingest logs that contain the process GUID, process name, and parent process. Additionally, you must ingest complete command-line executions. These logs must be processed using the appropriate Splunk Technology Add-ons that are specific to the EDR product. The logs must also be mapped to the Processes
node of the Endpoint
data model. Use the Splunk Common Information Model (CIM) to normalize the field names and speed up the data modeling process.
Known False Positives
unknown
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
A $process_name$ did a suspicious ping to invalid IP address on $dest$ | 36 | 60 | 60 |
References
Detection Testing
Test Type | Status | Dataset | Source | Sourcetype |
---|---|---|---|---|
Validation | ✅ Passing | N/A | N/A | N/A |
Unit | ✅ Passing | Dataset | XmlWinEventLog:Microsoft-Windows-Sysmon/Operational |
xmlwineventlog |
Integration | ✅ Passing | Dataset | XmlWinEventLog:Microsoft-Windows-Sysmon/Operational |
xmlwineventlog |
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: 2