This analytic is to detect an application try to connect and create ADSI Object to do LDAP query. Every time an application connects to the directory and attempts to create an ADSI object, the Active Directory Schema is checked for changes. If it has changed since the last connection, the schema is downloaded and stored in a cache on the local computer either in %LOCALAPPDATA%\Microsoft\Windows\SchCache or %systemroot%\SchCache. We found this a good anomaly use case to detect suspicious application like blackmatter ransomware that use ADS object api to execute ldap query. having a good list of ldap or normal AD query tool used within the network is a good start to reduce the noise.
- Type: Anomaly
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-09-07
- Author: Teoderick Contreras, Splunk
- ID: 991eb510-0fc6-11ec-82d3-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 `sysmon` EventCode=11 TargetFilename = "*\\Windows\\SchCache\\*" TargetFilename = "*.sch*" NOT (Image IN ("*\\Windows\\system32\\mmc.exe")) |stats count min(_time) as firstTime max(_time) as lastTime by Image TargetFilename EventCode process_id process_name dest | `security_content_ctime(firstTime)` | `security_content_ctime(lastTime)` | `schcache_change_by_app_connect_and_create_adsi_object_filter`
The SPL above uses the following Macros:
schcache_change_by_app_connect_and_create_adsi_object_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
To successfully implement this search, you need to be ingesting logs with the process name, parent process, and command-line executions from your endpoints. If you are using Sysmon, you must have at least version 6.0.4 of the Sysmon TA.
Known False Positives
normal application like mmc.exe and other ldap query tool may trigger this detections.
Associated Analytic Story
|25.0||50||50||process $Image$ create a file $TargetFilename$ in host $dest$|
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
source | version: 1