: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

This analytic identifies high resource utilization anomalies in Kubernetes processes. It uses process metrics from an OTEL collector and hostmetrics receiver, fetched from Splunk Observability cloud via the Splunk Infrastructure Monitoring Add-on. The detection uses a lookup table with average and standard deviation values for various process metrics to identify anomalies. High resource utilization can indicate security threats or operational issues, such as cryptojacking, unauthorized data exfiltration, or compromised containers. These anomalies can disrupt services, exhaust resources, increase costs, and allow attackers to evade detection or maintain access.

  • Type: Anomaly

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

  • Last Updated: 2023-12-18
  • Author: Matthew Moore, Splunk
  • ID: 25ca9594-7a0d-4a95-a5e5-3228d7398ec8

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 avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s 
| eval key = 'k8s.cluster.name' + ":" + 'host.name' + ":" + 'process.executable.name' 
| lookup k8s_process_resource_baseline key 
| fillnull 
| eval anomalies = "" 
| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring('<<MATCHSTR>>') + " avg_<<MATCHSTR>>=" + tostring('avg_<<MATCHSTR>>') + " 'stdev_<<MATCHSTR>>'=" + tostring('stdev_<<MATCHSTR>>') + ", " , anomalies) ] 
| eval anomalies = replace(anomalies, ",\s$", "") 
| where anomalies!="" 
| stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name 
| sort - count 
| where count > 5 
| rename host.name as host 
| `kubernetes_process_with_anomalous_resource_utilisation_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_process_with_anomalous_resource_utilisation_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Lookups

The SPL above uses the following Lookups:

Required fields

List of fields required to use this analytic.

  • process.*
  • 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 Process with Anomalous Resource Utilisation 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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