: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 changes in network communication behavior in a Kubernetes container by examining inbound to outbound network IO ratios. It uses process metrics from an OTEL collector and Kubelet Stats Receiver, and data from Splunk Observability cloud via the Splunk Infrastructure Monitoring Add-on. A lookup table containing average and standard deviation for network IO is used to evaluate anomalies for each container. An event is generated if the anomaly persists over a 1 hour period. These anomalies may indicate security threats such as data exfiltration, command and control communication, or compromised container behavior. They can compromise the confidentiality, availability, and integrity of applications and data, necessitating rapid detection and response. Anomalous network utilization may suggest a compromised container, potentially leading to data breaches, service outages, and unauthorized access within the Kubernetes cluster.

  • Type: Anomaly

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

  • Last Updated: 2023-12-19
  • Author: Matthew Moore, Splunk
  • ID: 9d8f6e3f-39df-46d8-a9d4-96173edc501f

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(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s 
| eval service = replace('k8s.pod.name', "-\w{5}$
|-[abcdef0-9]{8,10}-\w{5}$", "") 
| eval key = 'k8s.cluster.name' + ":" + 'service' 
| stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key service k8s.cluster.name k8s.pod.name k8s.node.name _time 
| eval inbound:outbound = inbound_network_io/outbound_network_io 
| eval outbound:inbound = outbound_network_io/inbound_network_io 
| fields - *network_io 
| lookup k8s_container_network_io_ratio_baseline key 
| eval anomalies = "" 
| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> ratio higher than average by " + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring('<<MATCHSTR>>') + " avg_<<MATCHSTR>>=" + tostring('avg_<<MATCHSTR>>') + " 'stdev_<<MATCHSTR>>'=" + tostring('stdev_<<MATCHSTR>>') + ", " , anomalies) ] 
| eval anomalies = replace(anomalies, ",\s$", "") 
| where anomalies!="" 
| stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service 
| rename service as k8s.service 
| where count > 5 
| rename k8s.node.name as host 
| `kubernetes_anomalous_inbound_to_outbound_network_io_ratio_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_anomalous_inbound_to_outbound_network_io_ratio_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.

  • k8s.pod.network.io
  • direction
  • k8s.cluster.name
  • k8s.node.name
  • k8s.pod.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 Anomalous Inbound to Outbound Network IO Ratio from Container 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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