Name:Kubernetes Anomalous Inbound to Outbound Network IO Ratio id:9d8f6e3f-39df-46d8-a9d4-96173edc501f version:4 date:2024-10-17 author:Matthew Moore, Splunk status:experimental type:Anomaly 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. 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}$|-[abcdef0-9]{8,10}-\w{5}$", "") | eval key = 'k8s.cluster.name' + ":" + 'service' | stats avg(eval(if(direction="transmit", io,null()))) as outbound_network_io avg(eval(if(direction="receive", io,null()))) as inbound_network_io by key service k8s.cluster.name k8s.pod.name k8s.node.name _time | eval inbound:outbound = inbound_network_io/outbound_network_io | eval outbound:inbound = outbound_network_io/inbound_network_io | fields - *network_io | lookup k8s_container_network_io_ratio_baseline key | eval anomalies = "" | foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 4 * 'stdev_<<MATCHSTR>>'), anomalies + "<<MATCHSTR>> ratio 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_to_outbound_network_io_ratio_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 to Outbound Network IO Ratio 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
