: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

The following analytic identifies anomalous network traffic volumes between Kubernetes workloads or between a workload and external sources. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average over the past 30 days to identify significant deviations. This activity is significant as unexpected spikes may indicate unauthorized data transfers or lateral movement. If confirmed malicious, it could lead to data exfiltration or compromise of additional services, potentially resulting in data breaches.

  • Type: Anomaly

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

  • Last Updated: 2024-05-24
  • Author: Matthew Moore, Splunk
  • ID: 886c7e51-2ea1-425d-8705-faaca5a64cc6

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(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s 
| eval key='source.workload.name' + ":" + 'dest.workload.name' 
| join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name 
| eval key='source.workload.name' + ":" + 'dest.workload.name' ] 
| eval anomalies = "" 
| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 3 * '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) ] 
| fillnull 
| eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") 
| where anomalies!="" 
| stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name 
| rename service as k8s.service 
| where count > 5 
| rename k8s.cluster.name as host 
| `kubernetes_anomalous_traffic_on_network_edge_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_anomalous_traffic_on_network_edge_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • tcp.*
  • udp.*
  • k8s.cluster.name
  • source.workload.name
  • dest.workload.name
  • udp.packets

How To Implement

To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:

  • Name sim_npm_metrics_to_metrics_index
  • Metric Resolution 10000

    Known False Positives

    unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 Kubernetes Anomalous Traffic on Network Edge in kubernetes cluster $host$

: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

source | version: 2