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.

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This analytic detects newly seen process within the Kubernetes scope on a master or worker node. 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. The specific metric used by this detection is process.memory.utilization. Newly seen processes on a Kubernetes worker node are concerning as they may represent security risks and anomalies that could be related to unauthorized activity. New processes may be introduced in an attempt to compromise the node or gain control of the Kubernetes cluster. By detecting these processes, they can be investigated, and correlated with other anomalous activity for that host. Newly seen processes may be part of an attacker's strategy to compromise the node, gain unauthorized access, and subsequently extend their control to the entire Kubernetes cluster. These processes could facilitate activities such as data exfiltration, privilege escalation, denial-of-service attacks, or the introduction of malware and backdoors, putting sensitive data, applications, and the entire infrastructure at risk. The consequences may include data breaches, service disruptions, financial losses, and reputational damage, underscoring the need to identify anomalous process and associate them with any concurrent risk activity.

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

  • Last Updated: 2023-12-18
  • Author: Matthew Moore, Splunk
  • ID: c8119b2f-d7f7-40be-940a-1c582870e8e2




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.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.executable.name ] 
| stats count values(current) as current by host.name k8s.cluster.name k8s.node.name process.executable.name 
| where count=1 and current="True" 
| rename host.name as host 
| `kubernetes_previously_unseen_process_filter` 


The SPL above uses the following Macros:

:information_source: kubernetes_previously_unseen_process_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 Previously Unseen Process 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

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