| ID | Technique | Tactic |
|---|---|---|
| T1204 | User Execution | Execution |
Detection: Kubernetes Anomalous Inbound to Outbound Network IO Ratio
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 significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster.
Search
1
2| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s
3| eval service = replace('k8s.pod.name', "-\w{5}$
4|-[abcdef0-9]{8,10}-\w{5}$", "")
5| eval key = 'k8s.cluster.name' + ":" + 'service'
6| stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key service k8s.cluster.name k8s.pod.name k8s.node.name _time
7| eval inbound:outbound = inbound_network_io/outbound_network_io
8| eval outbound:inbound = outbound_network_io/inbound_network_io
9| fields - *network_io
10| lookup k8s_container_network_io_ratio_baseline key
11| eval anomalies = ""
12| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> ratio higher than average by " + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring('<<MATCHSTR>>') + " avg_<<MATCHSTR>>=" + tostring('avg_<<MATCHSTR>>') + " 'stdev_<<MATCHSTR>>'=" + tostring('stdev_<<MATCHSTR>>') + ", " , anomalies) ]
13| eval anomalies = replace(anomalies, ",\s$", "")
14| where anomalies!=""
15| stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service
16| rename service as k8s.service
17| where count > 5
18| rename k8s.node.name as host
19| `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_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_anomalous_inbound_to_outbound_network_io_ratio_filter | search * |
kubernetes_anomalous_inbound_to_outbound_network_io_ratio_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 Anomalous Inbound to Outbound Network IO Ratio from Container 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: 2