ID | Technique | Tactic |
---|---|---|
T1204 | User Execution | Execution |
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.
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.
Search
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
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
N/A | N/A | N/A | N/A | N/A |
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
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 |
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 |
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: 3