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baseline_of_kubernetes_container_network_io.yml
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baseline_of_kubernetes_container_network_io.yml
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name: Baseline Of Kubernetes Container Network IO
id: 6edaca1d-d436-42d0-8df0-6895d3bf5b70
version: 1
date: '2023-12-19'
author: Matthew Moore, Splunk
type: Baseline
datamodel: []
description: This baseline rule calculates the average and standard deviation of inbound and outbound network IO for each Kubernetes container.
It uses metrics from the Kubernetes API and the Splunk Infrastructure Monitoring Add-on. The rule generates a lookup table with the average and
standard deviation of the network IO for each container. This baseline can be used to detect anomalies in network communication behavior,
which may indicate security threats such as data exfiltration, command and control communication, or compromised container behavior.
search: '| 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 avg_outbound_network_io avg(eval(if(direction="receive", io,null()))) as avg_inbound_network_io
stdev(eval(if(direction="transmit", io,null()))) as stdev_outbound_network_io stdev(eval(if(direction="receive", io,null()))) as stdev_inbound_network_io
count latest(_time) as last_seen by key
| outputlookup k8s_container_network_io_baseline '
how_to_implement: 'To implement this detection, follow these steps:
1. Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
2. Enable the hostmetrics/process receiver in the OTEL configuration.
3. Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
4. Install the Splunk Infrastructure Monitoring (SIM) add-on (ref: https://splunkbase.splunk.com/app/5247)
5. Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
6. Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
7. In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
8. 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'')
9. Set the Metric Resolution to 10000.
10. Leave all other settings at their default values.'
known_false_positives: none
references: []
tags:
analytic_story:
- Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring
detections:
- Kubernetes Anomalous Inbound Outbound Network IO
product:
- Splunk Enterprise
- Splunk Enterprise Security
- Splunk Cloud
required_fields:
- k8s.pod.network.io
- k8s.cluster.name
- k8s.node.name
- k8s.pod.name
security_domain: network
deployment:
scheduling:
cron_schedule: 0 2 * * 0
earliest_time: -30d@d
latest_time: -1d@d
schedule_window: auto