| ID | Technique | Tactic |
|---|---|---|
| T1560 | Archive Collected Data | Collection |
Detection: Windows Archived Collected Data In TEMP Folder
Description
The following analytic detects the creation of archived files in a temporary folder, which may contain collected data. This behavior is often associated with malicious activity, where attackers compress sensitive information before exfiltration. The detection focuses on monitoring specific directories, such as temp folders, for the presence of newly created archive files (e.g., .zip, .rar, .tar). By identifying this pattern, security teams can quickly respond to potential data collection and exfiltration attempts, minimizing the risk of data breaches and improving overall threat detection.
Search
1
2|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime FROM datamodel=Endpoint.Filesystem where Filesystem.file_name IN ("*.zip", "*.rar", "*.tar", "*.7z") Filesystem.file_path = "*\\temp\\*" by _time Filesystem.file_name Filesystem.file_path Filesystem.dest Filesystem.file_create_time
3| `drop_dm_object_name(Filesystem)`
4| `security_content_ctime(firstTime)`
5| `security_content_ctime(lastTime)`
6| `windows_archived_collected_data_in_temp_folder_filter`
Data Source
No data sources specified for this detection.
Macros Used
| Name | Value |
|---|---|
| security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
| windows_archived_collected_data_in_temp_folder_filter | search * |
windows_archived_collected_data_in_temp_folder_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
KillChainPhase.EXPLOITAITON
NistCategory.DE_CM
Cis18Value.CIS_10
APT28
APT32
Axiom
Dragonfly
Ember Bear
FIN6
Ke3chang
Lazarus Group
Leviathan
LuminousMoth
Patchwork
menuPass
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 |
This configuration file applies to all detections of type TTP. These detections will use Risk Based Alerting and generate Notable Events.
Implementation
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 Processes node. In addition, confirm the latest CIM App 4.20 or higher is installed and the latest TA for the endpoint product.
Known False Positives
unknown
Associated Analytic Story
Risk Based Analytics (RBA)
| Risk Message | Risk Score | Impact | Confidence |
|---|---|---|---|
| A archive file [$file_name$] was creatd in %temp% folder on [$dest$]. | 64 | 80 | 80 |
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: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: 1