MacOS Data Chunking

Name:MacOS Data Chunking
id:7f1c8bed-9bd4-40b0-a1df-c262cbade0fc
version:4
date:None
author:Raven Tait, Splunk
status:production
type:Anomaly
Description:The following analytic detects suspicious data chunking activities that involve the use of split or dd, potentially indicating an attempt to evade detection by breaking large files into smaller parts. Attackers may use this technique to bypass size-based security controls, facilitating the covert exfiltration of sensitive data. By monitoring for unusual or unauthorized use of these commands, this analytic helps identify potential data exfiltration attempts, allowing security teams to intervene and prevent the unauthorized transfer of critical information from the network.
Data_source:

• -Osquery Results

search:| tstats `security_content_summariesonly`
count min(_time) as firstTime
max(_time) as lastTime

from datamodel=Endpoint.Processes where

(
Processes.process IN ("*/dd *", "dd *")
Processes.process = "* if=*"
)
OR
(
Processes.process = "*split *"
Processes.process="* -b *"
)

by Processes.dest Processes.original_file_name Processes.parent_process_id
Processes.process Processes.process_exec Processes.process_guid
Processes.process_hash Processes.process_id
Processes.process_current_directory Processes.process_name
Processes.process_path Processes.user
Processes.user_id Processes.vendor_product

| `drop_dm_object_name(Processes)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)`
| `macos_data_chunking_filter`

how_to_implement:This detection uses osquery and endpoint security on MacOS. Follow the link in references, which describes how to setup process auditing in MacOS with endpoint security and osquery. Also the [TA-OSquery](https://splunkbase.splunk.com/app/8574) must be deployed across your indexers and universal forwarders in order to have the osquery data populate the data models.
known_false_positives:Administrator or network operator can use this application for automation purposes. Please update the filter macros to remove false positives.
References:
  -https://osquery.readthedocs.io/en/stable/deployment/process-auditing/
  -https://ss64.com/mac/dd.html
  -https://ss64.com/mac/split.html
drilldown_searches:
 name:'View the detection results for - "$user$" and "$dest$"'
 search:'%original_detection_search% | search user = "$user$" dest = "$dest$"'
 earliest_offset:'$info_min_time$'
 latest_offset:'$info_max_time$'
 name:'View risk events for the last 7 days for - "$user$" and "$dest$"'
 search:'| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$user$", "$dest$") | stats count min(_time) as firstTime max(_time) as lastTime values(search_name) as "Search Name" values(risk_message) as "Risk Message" values(analyticstories) as "Analytic Stories" values(annotations._all) as "Annotations" values(annotations.mitre_attack.mitre_tactic) as "ATT&CK Tactics" by normalized_risk_object | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)`'
 earliest_offset:'7d'
 latest_offset:'0'
analytic_story:['MacOS Post-Exploitation']

asset_type:Endpoint

mitre_attack_id:['T1030']

product:['Splunk Enterprise', 'Splunk Enterprise Security', 'Splunk Cloud']

category:endpoint

security_domain:endpoint

tags:

tests:
 name:'True Positive Test'
 attack_data:
  data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1030/osquery_data_chunking/osquery.log
  source: osquery
  sourcetype: osquery:results
 test_type:'unit'
manual_test:None