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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.

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