Kubernetes Anomalous Inbound to Outbound Network IO Ratio
The following analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL…
Description
The following analytic identifies significant changes in network communication behavior within Kubernetes containers by examining the inbound to outbound network IO ratios. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. Anomalies are detected using a lookup table containing average and standard deviation values for network IO, triggering an event if the anomaly persists for over an hour. This activity is significant as it may indicate data exfiltration, command and control communication, or compromised container behavior. If confirmed malicious, it could lead to data breaches, service outages, and unauthorized access within the Kubernetes cluster.
Detection logic
Detection requirements
- Platform
- ContainersIaaSLinuxmacOSWindows
The rule states no platform. This is derived from the ATT&CK technique it maps to.
Known benign triggers
- No false positives have been identified at this time.
MITRE ATT&CK mappings
0 exclusive techniques.This is coverage no other published rule has; it is not this rule's total technique count.
References
| Reference | Cited by |
|---|---|
| github.com/signalfx/splunk-otel-collector-chart | 12 |
From the source
- At source
- Open at source
- Upstream identifier
- 9d8f6e3f-39df-46d8-a9d4-96173edc501f
- Tagged by the source as
- Abnormal Kubernetes Behavior using Splunk Infrastructure MonitoringKubernetescloudSplunk CloudSplunk EnterpriseSplunk Enterprise Securitynetwork
Licence
- Published under
- Apache License 2.0Read the licence
- Attribution
- Required
- Obtained under
- Apache-2.0Read the origin licence
Authorship
- Written by
- Published
- Aug 26, 2026
- Version
- 1