Detection: Kubernetes Process with Anomalous Resource Utilisation

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-17 ID: 25ca9594-7a0d-4a95-a5e5-3228d7398ec8 Author: Matthew Moore, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies high resource utilization anomalies in Kubernetes processes. It leverages process metrics from an OTEL collector and hostmetrics receiver, fetched via the Splunk Infrastructure Monitoring Add-on. The detection uses a lookup table with average and standard deviation values to spot anomalies. This activity is significant as high resource utilization can indicate security threats like cryptojacking, unauthorized data exfiltration, or compromised containers. If confirmed malicious, such anomalies can disrupt services, exhaust resources, increase costs, and allow attackers to evade detection or maintain access.

 1
 2| mstats avg(process.*) as process.* where `kubernetes_metrics` by host.name k8s.cluster.name k8s.node.name process.executable.name span=10s 
 3| eval key = 'k8s.cluster.name' + ":" + 'host.name' + ":" + 'process.executable.name' 
 4| lookup k8s_process_resource_baseline key 
 5| fillnull 
 6| eval anomalies = "" 
 7| 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) ] 
 8| eval anomalies = replace(anomalies, ",\s$", "") 
 9| where anomalies!="" 
10| stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name 
11| sort - count 
12| where count > 5 
13| rename host.name as host 
14| `kubernetes_process_with_anomalous_resource_utilisation_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
kubernetes_metrics index=kubernetes_metrics
kubernetes_process_with_anomalous_resource_utilisation_filter search *
kubernetes_process_with_anomalous_resource_utilisation_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1204 User Execution Execution
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
LAPSUS$
Scattered Spider

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

Implementation

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Kubernetes Process with Anomalous Resource Utilisation on host $host$ 25 50 50
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Not Applicable N/A N/A N/A
Unit ❌ Failing N/A N/A N/A
Integration ❌ Failing N/A N/A N/A

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: GitHub | Version: 4