Detection: Kubernetes newly seen TCP edge

EXPERIMENTAL DETECTION

This detection status is set to experimental. The Splunk Threat Research team has not yet fully tested, simulated, or built comprehensive datasets for this detection. As such, this analytic is not officially supported. If you have any questions or concerns, please reach out to us at research@splunk.com.

Updated Date: 2024-10-17 ID: 13f081d6-7052-428a-bbb0-892c79ca7c65 Author: Matthew Moore, Splunk Type: Anomaly Product: Splunk Enterprise Security

Description

The following analytic identifies newly seen TCP communication between source and destination workload pairs within a Kubernetes cluster. It leverages Network Performance Monitoring metrics collected via an OTEL collector and pulled from Splunk Observability Cloud. The detection compares network activity over the last hour with the past 30 days to spot new inter-workload communications. This is significant as new connections can indicate changes in application behavior or potential security threats. If malicious, unauthorized connections could lead to data breaches, privilege escalation, lateral movement, or disruption of critical services, compromising the application's integrity, availability, and confidentiality.

1
2| mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-1h by k8s.cluster.name source.workload.name dest.workload.name 
3| eval current="True" 
4| append [ mstats count(tcp.packets) as tcp.packets_count where `kubernetes_metrics` AND earliest=-30d latest=-1h by source.workload.name dest.workload.name 
5| eval current="false" ] 
6| eventstats values(current) as current by source.workload.name dest.workload.name 
7| search current="true" current!="false" 
8| rename k8s.cluster.name as host 
9| `kubernetes_newly_seen_tcp_edge_filter`

Data Source

No data sources specified for this detection.

Macros Used

Name Value
kubernetes_metrics index=kubernetes_metrics
kubernetes_newly_seen_tcp_edge_filter search *
kubernetes_newly_seen_tcp_edge_filter is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

Annotations

- MITRE ATT&CK
+ Kill Chain Phases
+ NIST
+ CIS
- Threat Actors
ID Technique Tactic
T1204 User Execution Execution
KillChainPhase.INSTALLATION
NistCategory.DE_AE
Cis18Value.CIS_13
LAPSUS$
Scattered Spider

Default Configuration

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

Setting Value
Disabled true
Cron Schedule 0 * * * *
Earliest Time -70m@m
Latest Time -10m@m
Schedule Window auto
Creates Risk Event True
This configuration file applies to all detections of type anomaly. These detections will use Risk Based Alerting.

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

Risk Based Analytics (RBA)

Risk Message Risk Score Impact Confidence
Kubernetes newly seen TCP edge in kubernetes cluster $host$ 25 50 50
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.

References

Detection Testing

Test Type Status Dataset Source Sourcetype
Validation Not Applicable N/A N/A N/A
Unit ❌ Failing N/A N/A N/A
Integration ❌ Failing N/A N/A N/A

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: 3