Kubernetes Anomalous Inbound Outbound Network IO
The following analytic identifies high inbound or outbound network I/O anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk…
Description
The following analytic identifies high inbound or outbound network I/O anomalies in Kubernetes containers. It leverages process metrics from an OTEL collector and Kubelet Stats Receiver, along with data from Splunk Observability Cloud. A lookup table with average and standard deviation values for network I/O is used to detect anomalies persisting over a 1-hour period. This activity is significant as it may indicate data exfiltration, command and control communication, or unauthorized data transfers. If confirmed malicious, it could lead to data breaches, service outages, financial losses, and reputational damage.
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
- 4f3b0c97-657e-4547-a89a-9a50c656e3cd
- 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