Detect Rare Executables

SPLUNK detection rule by Bhavin Patel, Splunk · Apache-2.0

The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe. It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs. This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack. If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact. The search currently identifies processes executed on fewer than 10 hosts, but this threshold can be adjusted based on the organization's environment and risk tolerance. The search groups results by process name which can lead to blind spots if a malicious process uses a common name. To mitigate this, consider enhancing the detection logic to group by additional attributes such as process hash.

name: Detect Rare Executables
id: 44fddcb2-8d3b-454c-874e-7c6de5a4f7ac
version: 14
creation_date: '2020-04-29'
modification_date: '2026-05-13'
author: Bhavin Patel, Splunk
status: production
type: Anomaly
description: |
    The following analytic detects the execution of rare processes that appear only once across the network within a specified timeframe.
    It leverages data from Endpoint Detection and Response (EDR) agents, focusing on process execution logs.
    This activity is significant for a SOC as it helps identify potentially malicious activities or unauthorized software, which could indicate a security breach or ongoing attack.
    If confirmed malicious, such rare processes could lead to data theft, privilege escalation, or complete system compromise, making early detection crucial for minimizing impact.
    The search currently identifies processes executed on fewer than 10 hosts, but this threshold can be adjusted based on the organization's environment and risk tolerance.
    The search groups results by process name which can lead to blind spots if a malicious process uses a common name. To mitigate this, consider enhancing the detection logic to group by additional attributes such as process hash.
data_source:
    - Sysmon EventID 1
    - Windows Event Log Security 4688
    - CrowdStrike ProcessRollup2
search: |
    | tstats `security_content_summariesonly`
      dc(Processes.dest) as dc_dest
      values(Processes.dest) as dest
      values(Processes.user) as user
      min(_time) as firstTime
      max(_time) as lastTime
      latest(Processes.action) as action
      values(Processes.original_file_name) as original_file_name
      values(Processes.parent_process) as parent_process
      values(Processes.parent_process_exec) as parent_process_exec
      latest(Processes.parent_process_guid) as parent_process_guid
      latest(Processes.parent_process_id) as parent_process_id
      values(Processes.parent_process_name) as parent_process_name
      values(Processes.parent_process_path) as parent_process_path
      values(Processes.process) as process
      values(Processes.process_exec) as process_exec
      latest(Processes.process_guid) as process_guid
      values(Processes.process_hash) as process_hash
      values(Processes.process_path) as process_path
      latest(Processes.process_id) as process_id
      latest(Processes.process_integrity_level) as process_integrity_level
      latest(Processes.user_id) as user_id
      latest(Processes.vendor_product) as vendor_product
    from datamodel=Endpoint.Processes
    by Processes.process_name
    | `drop_dm_object_name(Processes)`
    | search dc_dest < 10
    | `security_content_ctime(firstTime)`
    | `security_content_ctime(lastTime)`
    | `detect_rare_executables_filter`
how_to_implement: 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: |
    Some legitimate processes may be only rarely executed in your environment.
    Apply additional filters as needed.
references: []
drilldown_searches:
    - name: View the detection results for - "$dest$"
      search: '%original_detection_search% | search  dest = "$dest$"'
      earliest_offset: $info_min_time$
      latest_offset: $info_max_time$
    - name: View risk events for the last 7 days for - "$dest$"
      search: '| from datamodel Risk.All_Risk | search normalized_risk_object IN ("$dest$") | 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"
intermediate_findings:
    entities:
        - field: dest
          type: system
          score: 20
          message: A rare process - [$process_name$] has been detected on less than 10 hosts on $dest$.
threat_objects:
    - field: process_name
      type: process_name
analytic_story:
    - China-Nexus Threat Activity
    - Unusual Processes
    - SnappyBee
    - Salt Typhoon
    - Rhysida Ransomware
    - Crypto Stealer
asset_type: Endpoint
mitre_attack_id:
    - T1204
product:
    - Splunk Enterprise
    - Splunk Enterprise Security
    - Splunk Cloud
category: endpoint
security_domain: endpoint
tests:
    - name: True Positive Test
      attack_data:
        - data: https://media.githubusercontent.com/media/splunk/attack_data/master/datasets/attack_techniques/T1204/rare_executables/windows-sysmon.log
          source: XmlWinEventLog:Microsoft-Windows-Sysmon/Operational
          sourcetype: XmlWinEventLog
      test_type: unit

[[ currentRule.title && currentRule.title.length > 60 ? currentRule.title.slice(0, 60) + '…' : currentRule.title ]]

ae8a171c-86b9-4ffd-a011-7e93c966231a

Propose edits, view related rules, and engage with the community through comments.

Executable code, not a declarative rule

This submission is arbitrary [[ currentRule.format ? currentRule.format.toUpperCase() : '' ]] code, not a declarative detection rule — review it as you would any script before running it, and note that it cannot be safely aggregated or auto-executed alongside YARA/Sigma/Suricata/CRS rules.

Rejected pattern present

[[ reason ]]

What does this mean?

Format & documentation suggestions

Not a security issue — this rule is valid and was accepted as-is. These are optional fields/conventions its own format recommends, mainly affecting its quality score and how well it documents itself for other reviewers.

[[ reason ]]

What do these messages mean?

[[ currentRule.title && currentRule.title.length > 60 ? currentRule.title.slice(0, 60) + '…' : currentRule.title ]]

[[ currentRule.title ]]

Community Discussion