Name:Kubernetes Previously Unseen Container Image Name id:fea515a4-b1d8-4cd6-80d6-e0d71397b891 version:4 date:2024-10-17 author:Matthew Moore, Splunk status:experimental type:Anomaly Description:The following analytic identifies the creation of containerized workloads using previously unseen images in a Kubernetes cluster. It leverages process metrics from an OTEL collector and Kubernetes cluster receiver, pulled from Splunk Observability Cloud. The detection compares container image names seen in the last hour with those from the previous 30 days. This activity is significant as unfamiliar container images may introduce vulnerabilities, malware, or misconfigurations, posing threats to the cluster's integrity. If confirmed malicious, compromised images can lead to data breaches, service disruptions, unauthorized access, and potential lateral movement within the cluster. Data_source:
search:| mstats count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-24h by host.name k8s.cluster.name k8s.node.name container.image.name | eval current="True" | append [mstats count(k8s.container.ready) as k8s.container.ready_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by host.name k8s.cluster.name k8s.node.name container.image.name | eval current="false" ] | stats values(current) as current by host.name k8s.cluster.name k8s.node.name container.image.name | search current="true" AND current!="false" | rename host.name as host | `kubernetes_previously_unseen_container_image_name_filter`
how_to_implement:To implement this detection, follow these steps:
* Deploy the OpenTelemetry Collector (OTEL) to your Kubernetes cluster.
* Enable the hostmetrics/process receiver in the OTEL configuration.
* Ensure that the process metrics, specifically Process.cpu.utilization and process.memory.utilization, are enabled.
* Install the Splunk Infrastructure Monitoring (SIM) add-on. (ref: https://splunkbase.splunk.com/app/5247)
* Configure the SIM add-on with your Observability Cloud Organization ID and Access Token.
* Set up the SIM modular input to ingest Process Metrics. Name this input "sim_process_metrics_to_metrics_index".
* In the SIM configuration, set the Organization ID to your Observability Cloud Organization ID.
* Set the Signal Flow Program to the following: data('process.threads').publish(label='A'); data('process.cpu.utilization').publish(label='B'); data('process.cpu.time').publish(label='C'); data('process.disk.io').publish(label='D'); data('process.memory.usage').publish(label='E'); data('process.memory.virtual').publish(label='F'); data('process.memory.utilization').publish(label='G'); data('process.cpu.utilization').publish(label='H'); data('process.disk.operations').publish(label='I'); data('process.handles').publish(label='J'); data('process.threads').publish(label='K')
* Set the Metric Resolution to 10000.
* Leave all other settings at their default values.
* Run the Search Baseline Of Kubernetes Container Network IO Ratio known_false_positives:unknown References: -https://github.com/signalfx/splunk-otel-collector-chart drilldown_searches:
: tags: analytic_story: - 'Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring' asset_type:Kubernetes confidence:50 impact:50 message:Kubernetes Previously Unseen Container Image Name on host $host$ mitre_attack_id: - 'T1204' observable: name:'host' type:'Hostname' - role: - 'Victim' product: - 'Splunk Enterprise' - 'Splunk Enterprise Security' - 'Splunk Cloud' required_fields: - 'k8s.container.ready_count' - 'host.name' - 'k8s.cluster.name' - 'k8s.node.name' risk_score:25 security_domain:network
