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Description

The following analytic detects potential scanning activities within a Kubernetes environment. It identifies unauthorized access attempts, probing of public APIs, or attempts to exploit known vulnerabilities. The analytic detects this behavior by monitoring Kubernetes audit logs for patterns indicative of scanning, such as repeated failed access attempts or unusual API requests. This behavior is worth identifying for a SOC as it could indicate an attackers preliminary step in an attack, aiming to gather information about the system to find potential vulnerabilities. The impact of such an attack could be severe, potentially leading to unauthorized access to sensitive systems or data.

  • Type: Anomaly

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

  • Last Updated: 2023-12-07
  • Author: Patrick Bareiss, Splunk
  • ID: 2f4abe6d-5991-464d-8216-f90f42999764

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1046 Network Service Discovery Discovery
Kill Chain Phase
  • Exploitation
NIST
  • DE.AE
CIS20
  • CIS 13
CVE 


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`kube_audit` "user.groups{}"="system:unauthenticated" "responseStatus.code"=403 
| iplocation sourceIPs{} 
| stats count values(userAgent) as userAgent values(user.username) as user.username values(user.groups{}) as user.groups{} values(verb) as verb values(requestURI) as requestURI values(responseStatus.code) as responseStatus.code values(responseStatus.message) as responseStatus.message values(responseStatus.reason) as responseStatus.reason values(responseStatus.status) as responseStatus.status by sourceIPs{} Country City 
| where count > 5 
| rename sourceIPs{} as src_ip, user.username as user 
| `kubernetes_access_scanning_filter`

Macros

The SPL above uses the following Macros:

:information_source: kubernetes_access_scanning_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.

  • verb
  • requestReceivedTimestamp
  • requestURI
  • responseStatus.code
  • sourceIPs{}
  • user.groups{}
  • user.username
  • userAgent
  • verb
  • responseStatus.reason
  • responseStatus.status

How To Implement

The detection is based on data that originates from Kubernetes Audit logs. Ensure that audit logging is enabled in your Kubernetes cluster. Kubernetes audit logs provide a record of the requests made to the Kubernetes API server, which is crucial for monitoring and detecting suspicious activities. Configure the audit policy in Kubernetes to determine what kind of activities are logged. This is done by creating an Audit Policy and providing it to the API server. Use the Splunk OpenTelemetry Collector for Kubernetes to collect the logs. This doc will describe how to collect the audit log file https://github.com/signalfx/splunk-otel-collector-chart/blob/main/docs/migration-from-sck.md.

Known False Positives

unknown

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
49.0 70 70 Kubernetes scanning from ip $src_ip$

: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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