Detection: Kubernetes Anomalous Outbound Network Activity from Process

Description

The following analytic identifies anomalously high outbound network activity from processes running within containerized workloads in a Kubernetes environment. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares recent network metrics (tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets) over the last hour with the average metrics over the past 30 days. This activity is significant as it may indicate data exfiltration, process modification, or container compromise. If confirmed malicious, it could lead to unauthorized data exfiltration, communication with malicious entities, or further attacks within the containerized environment.

Search

 1
 2| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name source.process.name  span=10s 
 3| eval key='source.workload.name' + ":" + 'source.process.name' 
 4| join type=left key [ mstats avg(tcp.*) as avg_tcp.* avg(udp.*) as avg_udp.* stdev(tcp.*) as stdev_tcp.* avg(udp.*) as stdev_udp.* where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name source.process.name 
 5| eval key='source.workload.name' + ":" + 'source.process.name' ] 
 6| eval anomalies = "" 
 7| foreach stdev_* [ eval anomalies =if( '<<MATCHSTR>>' > ('avg_<<MATCHSTR>>' + 3 * '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) ] 
 8| fillnull 
 9| eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") 
10| where anomalies!="" 
11| stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name source.process.name 
12| where count > 5 
13| rename k8s.cluster.name as host 
14| `kubernetes_anomalous_outbound_network_activity_from_process_filter`

Data Source

Macros Used

Annotations

Default Configuration

This detection is configured by default in Splunk Enterprise Security to run with the following settings:

Implementation

To gather NPM metrics the Open Telemetry to the Kubernetes Cluster and enable Network Performance Monitoring according to instructions found in Splunk Docs https://docs.splunk.com/observability/en/infrastructure/network-explorer/network-explorer-setup.html#network-explorer-setup In order to access those metrics from within Splunk Enterprise and ES, the Splunk Infrastructure Monitoring add-on must be installed and configured on a Splunk Search Head. Once installed, first configure the add-on with your O11y Cloud Org ID and Access Token. Lastly set up the add-on to ingest metrics from O11y cloud using the following settings, and any other settings left at default:

• Name sim_npm_metrics_to_metrics_index
• Metric Resolution 10000

Known False Positives

unknown

Associated Analytic Story

• Abnormal Kubernetes Behavior using Splunk Infrastructure Monitoring

Risk Based Analytics (RBA)

References

• https://github.com/signalfx/splunk-otel-collector-chart

Detection Testing

Replay any dataset to Splunk Enterprise by using our replay.py tool or the UI. Alternatively you can replay a dataset into a Splunk Attack Range

Source: GitHub | Version: 2