baseline_of_kubernetes_container_network_io_ratio.yml

name: Baseline Of Kubernetes Container Network IO Ratio
id: f395003b-6389-4e14-89bf-ac4dbea215bd
version: 2
date: '2024-09-24'
author: Matthew Moore, Splunk
type: Baseline
datamodel: []
description: This baseline rule calculates the average ratio of inbound to 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 ratio 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 outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key _time 
  | eval inbound:outbound = inbound_network_io/outbound_network_io 
  | eval outbound:inbound = outbound_network_io/inbound_network_io 
  | stats avg(*:*) as avg_*:* stdev(*:*) as stdev_*:*
    count latest(_time) as last_seen by key 
  | outputlookup k8s_container_network_io_ratio_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 to Outbound Network IO Ratio
  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