Kubernetes Anomalous Inbound Outbound Network IO

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 high Inbound or Outbound Network IO anomalies in a Kubernetes container. 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, service disruptions, or unauthorized data transfers. 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, financial losses, and reputational damage.

Type: Anomaly
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
Last Updated: 2023-12-19
Author: Matthew Moore, Splunk
ID: 4f3b0c97-657e-4547-a89a-9a50c656e3cd

Annotations

ATT&CK

ID	Technique	Tactic
T1204	User Execution	Execution

Kill Chain Phase

Installation

NIST

DE.AE

CIS20

CIS 13

CVE

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}$$
|-[abcdef0-9]{8,10}-\w{5}$$", "") 
| stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name service _time 
| eval key = 'k8s.cluster.name' + ":" + 'service' 
| lookup k8s_container_network_io_baseline key 
| eval anomalies = "" 
| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> 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_outbound_network_traffic_io_filter` 

Macros

The SPL above uses the following Macros:

kubernetes_metrics

kubernetes_anomalous_inbound_outbound_network_io_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:

k8s_container_network_io_baseline with data

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

Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring

RBA

Risk Score	Impact	Confidence	Message
25.0	50	50	Kubernetes Anomalous Inbound Outbound Network IO from container on host $host$

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

https://github.com/signalfx/splunk-otel-collector-chart

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

source | version: 1

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