: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 newly seen TCP communication between source and destination workload pairs within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to spot new inter-workload communications. This is significant as new connections can indicate changes in application behavior or potential security threats. If malicious, unauthorized connections could lead to data breaches, privilege escalation, lateral movement, or disruption of critical services, compromising the application's integrity, availability, and confidentiality.

  • Type: Anomaly

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

  • Last Updated: 2024-05-15
  • Author: Matthew Moore, Splunk
  • ID: 13f081d6-7052-428a-bbb0-892c79ca7c65

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 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`

Macros

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
  • Metric Resolution 10000

    Known False Positives

    unknown

Associated Analytic Story

RBA

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.

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