Detection: Excessive distinct processes from Windows Temp

Description

The following analytic identifies an excessive number of distinct processes executing from the Windows\Temp directory. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process paths and counts within a 20-minute window. This behavior is significant as it often indicates the presence of post-exploit frameworks like Koadic and Meterpreter, which use this technique to execute malicious actions. If confirmed malicious, this activity could allow attackers to execute arbitrary code, escalate privileges, and maintain persistence within the environment, posing a severe threat to system integrity and security.

1
2| tstats `security_content_summariesonly` values(Processes.process) as process distinct_count(Processes.process) as distinct_process_count  min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process_path = "*\\Windows\\Temp\\*" by Processes.dest Processes.user  _time span=20m 
3| where distinct_process_count > 37 
4| `drop_dm_object_name(Processes)` 
5| `security_content_ctime(firstTime)` 
6| `security_content_ctime(lastTime)` 
7| `excessive_distinct_processes_from_windows_temp_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($field$)
excessive_distinct_processes_from_windows_temp_filter search *
excessive_distinct_processes_from_windows_temp_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
T1059 Command and Scripting Interpreter Execution
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_10
APT19
APT32
APT37
APT39
Dragonfly
FIN5
FIN6
FIN7
Fox Kitten
Ke3chang
OilRig
Stealth Falcon
Whitefly
Windigo

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

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

Many benign applications will create processes from executables in Windows\Temp, although unlikely to exceed the given threshold. Filter as needed.

Associated Analytic Story

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Multiple processes were executed out of windows\temp within a short amount of time on $dest$. 80 80 100
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 Passing N/A N/A N/A
Unit Passing Dataset XmlWinEventLog:Security XmlWinEventLog
Integration ✅ Passing Dataset XmlWinEventLog:Security 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: 4