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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This detection detects inbound network traffic volume anomalies from processes running within containerised workloads. Anomalies are provided with context identifying the Kubernetes cluster, the workload name, and the type of anomaly.This detection leverages Network performance Monitoring metrics harvested using an OTEL collector, and is pulled from Splunk Observability cloud using the Splunk Infrastructure Monitoring Add-on. (https://splunkbase.splunk.com/app/5247). This detection compares the tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets metrics for destination (receiving) workload process pairs over the last 1 hour, with the average of those metrics for those pairs over the last 30 days in order to detect any anonymously high inbound network activity. Anomalies in inbound network traffic may suggest that the container is receiving unexpected or unauthorized data, potentially indicative of a breach, a vulnerability exploitation attempt, an attempt to overload the service, or propagation of malware. Successful compromise of a containerised application resulting in the ability to upload data, can result in installation of command and control software or other malware, data integrity damage, container escape, and further compromise of the environment. Additionally this kind of activity may result in resource contention, performance degradation and disruption to the normal operation of the environment.

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

  • Last Updated: 2024-01-10
  • Author: Matthew Moore, Splunk
  • ID: 10442d8b-0701-4c25-911d-d67b906e713c




ID Technique Tactic
T1204 User Execution Execution
Kill Chain Phase
  • Installation
  • DE.AE
  • CIS 13
| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name dest.workload.name dest.process.name  span=10s 
| eval key='dest.workload.name' + ":" + 'dest.process.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 dest.workload.name dest.process.name 
| eval key='dest.workload.name' + ":" + 'dest.process.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 dest.workload.name dest.process.name 
| where count > 5 
| rename k8s.cluster.name as host 
| `kubernetes_anomalous_inbound_network_activity_from_process_filter` 


The SPL above uses the following Macros:

:information_source: kubernetes_anomalous_inbound_network_activity_from_process_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
  • dest.process.name
  • dest.workload.name

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
  • Org ID <Your O11y Cloud Org Id>
  • Signal Flow Program data('tcp.packets').publish(label='A'); data('tcp.bytes').publish(label='B'); data('tcp.new_sockets').publish(label='C'); data('udp.packets').publish(label='D'); data('udp.bytes').publish(label='E')
  • Metric Resolution 10000

    Known False Positives


Associated Analytic Story


Risk Score Impact Confidence Message
25.0 50 50 Kubernetes Anomalous Inbound Network Activity from Process 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.


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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