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.

Try in Splunk Security Cloud


This analytic detects processes running within the same scope as Kubernetes that have been run from a newly seen path. This detection leverages process metrics harvested using an OTEL collector and hostmetrics receiever, and is pulled from Splunk Observability cloud using the Splunk Infrastructure Monitoring Add-on. (https://splunkbase.splunk.com/app/5247). This detection compares the processes seen for each node over the previous 1 hour with those over the previous 30 days up until the previous 1 hour, and alerts if the path for that process was not seen over the previous 30 days. The specific metric used by this detection is process.memory.utilization. Processes running from a newly seen path can signify potential security risks and anomalies. A process executing from an unfamiliar file path may indicate unauthorized changes to the file system, a compromised node, or the introduction of malicious software. If the presence of a process running from a newly seen file path on a Kubernetes node indicates malicious activity, the security implications could be severe. It suggests that an attacker has potentially compromised the node, allowing them to execute unauthorized processes and potentially gain control over critical resources. This could lead to further exploitation, data exfiltration, privilege escalation, or the introduction of malware and backdoors within the Kubernetes cluster.

  • Type: Anomaly
  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2023-12-18
  • Author: Matthew Moore, Splunk
  • ID: 454076fb-0e9e-4adf-b93a-da132621c5e6




ID Technique Tactic
T1204 User Execution Execution
Kill Chain Phase
  • Installation
  • DE.AE
  • CIS 13
| mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name 
| eval current="True" 
| append [ mstats count(process.memory.utilization) as process.memory.utilization_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name process.pid process.executable.path process.executable.name ] 
| stats count values(current) as current by host.name k8s.cluster.name k8s.node.name process.pid process.executable.name process.executable.path 
| where count=1 and current="True" 
| rename host.name as host 
| `kubernetes_process_running_from_new_path_filter` 


The SPL above uses the following Macros:

:information_source: kubernetes_process_running_from_new_path_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.

  • process.memory.utilization
  • host.name
  • k8s.cluster.name
  • k8s.node.name
  • process.executable.name

How To Implement

To implement this detection, follow these steps:

  • Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
  • Enable the hostmetrics/process receiver in the OTEL configuration.
  • Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
  • Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
  • Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
  • Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
  • In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
  • Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')
  • Set the Metric Resolution to 10000.
  • Leave all other settings at their default values.
  • Run the Search Baseline Of Kubernetes Container Network IO Ratio

    Known False Positives


Associated Analytic Story


Risk Score Impact Confidence Message
25.0 50 50 Kubernetes Process Running From New Path on host $host$

: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.


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

source | version: 1