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.

Try in Splunk Security Cloud


This analytic detects TCP communication between a newly seen source and destination workload pair. This is done to identify changes in network behavior between workloads in a kubernetes cluster. 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 network activity between workloads over the last 1 hour, with those over the last 30 days in order to detect newly seen inter workload communication. Newly seen network connections in a microservices based app indicate a change in behavior which could indicate potential security threats or anomalies. Distributed applications typically have common established network connection topologies, and new connections are often either an indication of a change in the application or an active threat. Unauthorized connections may enable the attacker to infiltrate the applications ecosystem, potentially leading to data breaches, manipulation of sensitive information, or disruption of critical services. Bad actors may exploit these connections to gain access, escalate privileges, move laterally within the microservices, or introduce malicious code or payloads, putting the applications integrity, availability, and confidentiality at risk.

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

  • Last Updated: 2024-01-10
  • Author: Matthew Moore, Splunk
  • ID: 13f081d6-7052-428a-bbb0-892c79ca7c65




ID Technique Tactic
T1204 User Execution Execution
Kill Chain Phase
  • Installation
  • DE.AE
  • CIS 13
| mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name 
| eval current="True" 
| append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name 
| eval current="false" ] 
| eventstats values(current) as current by source.workload.name dest.workload.name 
| search current="true" current!="false" 
| rename k8s.cluster.name as host 
| `kubernetes_newly_seen_tcp_edge_filter` 


The SPL above uses the following Macros:

:information_source: kubernetes_newly_seen_tcp_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.

  • k8s.cluster.name
  • source.workload.name
  • dest.workload.name
  • tcp.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
  • 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 newly seen TCP 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.


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