:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

This detection detects a high amount of office file copied. This can be an indicator for a malicious insider.

  • Type: Anomaly
  • Product: Splunk Behavioral Analytics
  • Datamodel: Endpoint_Filesystem
  • Last Updated: 2021-12-07
  • Author: Patrick Bareiss, Splunk
  • ID: 3c6594a9-8df6-45a1-9357-d73b62083c63

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1048.003 Exfiltration Over Unencrypted Non-C2 Protocol Exfiltration
Kill Chain Phase
  • Exploitation
NIST
CIS20
CVE
1
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3
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5
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11
| from read_ssa_enriched_events() 
| eval timestamp=parse_long(ucast(map_get(input_event, "_time"), "string", null)) 
| eval action=ucast(map_get(input_event, "action"), "string", null), process=ucast(map_get(input_event, "process"), "string", null), file_name=ucast(map_get(input_event, "file_name"), "string", null), file_path=ucast(map_get(input_event, "file_path"), "string", null), dest_user_id=ucast(map_get(input_event, "dest_user_id"), "string", null), dest_device_id=ucast(map_get(input_event, "dest_device_id"), "string", null) 
| where "Endpoint_Filesystem" IN(_datamodels) 
| where action="created" 
| where like(file_name, "%.doc%") OR like(file_name, "%.xls%") OR like(file_name, "%.ppt%") 
| stats count(file_name) AS count BY dest_user_id, dest_device_id, span(timestamp, 10m) 
| where count > 20 
| eval start_time=window_start, end_time=window_end, entities=mvappend(dest_user_id, dest_device_id), body=create_map(["count", count]) 
| into write_ssa_detected_events();

Macros

The SPL above uses the following Macros:

:information_source: excessive_number_of_office_files_copied_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.

  • action
  • process
  • file_name
  • file_path

How To Implement

To successfully implement this search you need to be ingesting information on process that include the name of the process responsible for the changes from your endpoints into the Endpoint datamodel in the Filesytem node.

Known False Positives

user may copy a lot of office fies from one folder to another

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
72.0 90 80 High number of files copied

:information_source: 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

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