:warning: THIS IS A EXPERIMENTAL DETECTION

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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Description

The following analytic detects previously unseen processes within the Kubernetes environment on master or worker nodes. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, and data is pulled from Splunk Observability Cloud. This detection compares processes observed in the last hour against those seen in the previous 30 days. Identifying new processes is crucial as they may indicate unauthorized activity or attempts to compromise the node. If confirmed malicious, these processes could lead to data exfiltration, privilege escalation, denial-of-service attacks, or the introduction of malware, posing significant risks to the Kubernetes cluster.

  • Type: Anomaly

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

  • Last Updated: 2024-05-13
  • Author: Matthew Moore, Splunk
  • ID: c8119b2f-d7f7-40be-940a-1c582870e8e2

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1204 User Execution Execution
Kill Chain Phase
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| 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`

Macros

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

    unknown

Associated Analytic Story

RBA

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.

Reference

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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