:warning: THIS IS A EXPERIMENTAL DETECTION

This detection has been marked experimental by the Splunk Threat Research team. This means we have not been able to test, simulate, or build datasets for this detection. Use at your own risk. This analytic is NOT supported.

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Description

This detection detects network traffic volume anomalies between workloads in a microservices hosted application, or between a workload and the outside world if the workload is shown as (unknown). This detection leverages Network performance Monitoring metrics harvested using an OTEL collector, and is pulled from Splunk Observability cloud using the Splunk Infrastructure Monitoring Add-on (https://splunkbase.splunk.com/app/5247). This detection compares the tcp.bytes, tcp.new_sockets, tcp.packets, udp.bytes, udp.packets metrics between workloads over the last 1 hour, with the average of those metrics over the last 30 days in order to detect any anonymously high inbound or outbound network activity. Unexpected spikes in network traffic may signify unauthorized data transfers, or abnormal behavior within the microservices ecosystem. Such activity might signify data exfiltration, unauthorized lateral movement, within the microservices environment. If a bad actor is responsible for this traffic they could compromise additional services or extract sensitive data, potentially leading to data breaches.

  • Type: Anomaly

  • Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud

  • Last Updated: 2024-01-10
  • Author: Matthew Moore, Splunk
  • ID: 886c7e51-2ea1-425d-8705-faaca5a64cc6

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1204 User Execution Execution
Kill Chain Phase
  • Installation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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| mstats avg(tcp.*) as tcp.* avg(udp.*) as udp.* where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name span=10s 
| eval key='source.workload.name' + ":" + 'dest.workload.name' 
| 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 dest.workload.name 
| eval key='source.workload.name' + ":" + 'dest.workload.name' ] 
| eval anomalies = "" 
| 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) ] 
| fillnull 
| eval anomalies = split(replace(anomalies, ",\s$$$$", "") ,", ") 
| where anomalies!="" 
| stats count(anomalies) as count values(anomalies) as anomalies by k8s.cluster.name source.workload.name dest.workload.name 
| rename service as k8s.service 
| where count > 5 
| rename k8s.cluster.name as host 
| `kubernetes_anomalous_traffic_on_network_edge_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_anomalous_traffic_on_network_edge_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Required fields

List of fields required to use this analytic.

  • tcp.*
  • udp.*
  • k8s.cluster.name
  • source.workload.name
  • dest.workload.name
  • udp.packets

How To Implement

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 \
  • Org ID <Your O11y Cloud Org Id> \
  • Signal Flow Program data('tcp.packets').publish(label='A'); data('tcp.bytes').publish(label='B'); data('tcp.new_sockets').publish(label='C'); data('udp.packets').publish(label='D'); data('udp.bytes').publish(label='E') \
  • Metric Resolution 10000

    Known False Positives

    unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 Kubernetes Anomalous Traffic on Network Edge in kubernetes cluster $host$

:information_source: The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

Reference

Test Dataset

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

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