| ID | Technique | Tactic |
|---|---|---|
| T1204 | User Execution | Execution |
Detection: Kubernetes Process with Resource Ratio Anomalies
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 detects anomalous changes in resource utilization ratios for processes running on a Kubernetes node. It leverages process metrics collected via an OTEL collector and hostmetrics receiver, analyzed through Splunk Observability Cloud. The detection uses a lookup table containing average and standard deviation values for various resource ratios (e.g., CPU:memory, CPU:disk operations). Significant deviations from these baselines may indicate compromised processes, malicious activity, or misconfigurations. If confirmed malicious, this could signify a security breach, allowing attackers to manipulate workloads, potentially leading to data exfiltration or service disruption.
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 cpu:mem = 'process.cpu.utilization'/'process.memory.utilization'
4| eval cpu:disk = 'process.cpu.utilization'/'process.disk.operations'
5| eval mem:disk = 'process.memory.utilization'/'process.disk.operations'
6| eval cpu:threads = 'process.cpu.utilization'/'process.threads'
7| eval disk:threads = 'process.disk.operations'/'process.threads'
8| eval key = 'k8s.cluster.name' + ":" + 'host.name' + ":" + 'process.executable.name'
9| lookup k8s_process_resource_ratio_baseline key
10| fillnull
11| eval anomalies = ""
12| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> ratio 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) ]
13| eval anomalies = replace(anomalies, ",\s$", "")
14| where anomalies!=""
15| stats count values(anomalies) as anomalies by host.name k8s.cluster.name k8s.node.name process.executable.name
16| where count > 5
17| rename host.name as host
18| `kubernetes_process_with_resource_ratio_anomalies_filter`
Data Source
No data sources specified for this detection.
Macros Used
| Name | Value |
|---|---|
| kubernetes_metrics | index=kubernetes_metrics |
| kubernetes_process_with_resource_ratio_anomalies_filter | search * |
kubernetes_process_with_resource_ratio_anomalies_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
MITRE ATT&CK
Kill Chain Phases
NIST
CIS
Threat Actors
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
LAPSUS$
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 Resource Ratio Anomalies 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