Name:Kubernetes Anomalous Inbound Outbound Network IO id:4f3b0c97-657e-4547-a89a-9a50c656e3cd version:4 date:2024-10-17 author:Matthew Moore, Splunk status:experimental type:Anomaly 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. Data_source:
search:| mstats avg(k8s.pod.network.io) as io where `kubernetes_metrics` by k8s.cluster.name k8s.pod.name k8s.node.name direction span=10s | eval service = replace('k8s.pod.name', "-\w{5}$$|-[abcdef0-9]{8,10}-\w{5}$$", "") | stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by k8s.cluster.name k8s.node.name k8s.pod.name service _time | eval key = 'k8s.cluster.name' + ":" + 'service' | lookup k8s_container_network_io_baseline key | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> higher than average by " + tostring(round(('<<MATCHSTR>>' - 'avg_<<MATCHSTR>>')/'stdev_<<MATCHSTR>>' ,2)) + " Standard Deviations. <<MATCHSTR>>=" + tostring('<<MATCHSTR>>') + " avg_<<MATCHSTR>>=" + tostring('avg_<<MATCHSTR>>') + " 'stdev_<<MATCHSTR>>'=" + tostring('stdev_<<MATCHSTR>>') + ", " , anomalies) ] | eval anomalies = replace(anomalies, ",\s$$", "") | where anomalies!="" | stats count values(anomalies) as anomalies by k8s.cluster.name k8s.node.name k8s.pod.name service | rename service as k8s.service | where count > 5 | rename k8s.node.name as host | `kubernetes_anomalous_inbound_outbound_network_io_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 Anomalous Inbound Outbound Network IO from container 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.pod.network.io' - 'direction' - 'k8s.cluster.name' - 'k8s.node.name' - 'k8s.pod.name' risk_score:25 security_domain:network
