Gsuite suspicious calendar invite
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.
This search can help the detection of compromised accounts or internal users sending suspcious calendar invites via GSuite calendar. These invites may contain malicious links or attachments.
- Type: Hunting
Product: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- Last Updated: 2021-10-24
- Author: Rod Soto, Teoderick Contreras
- ID: 03cdd68a-34fb-11ec-9bd3-acde48001122
Kill Chain Phase
- CIS 10
1 2 3 4 5 6 7 `gsuite_calendar` |bin span=5m _time |rename parameters.* as * |search target_calendar_id!=null email="*yourdomain.com" | stats count values(target_calendar_id) values(event_title) values(event_guest) by email _time | where count >100 | `gsuite_suspicious_calendar_invite_filter`
The SPL above uses the following Macros:
gsuite_suspicious_calendar_invite_filter is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
List of fields required to use this analytic.
How To Implement
In order to successfully implement this search, you need to be ingesting logs related to gsuite (gsuitejson) having the file sharing metadata like file type, source owner, destination target user, description, etc. This search can also be made more specific by selecting specific emails, subdomains timeframe, organizational units, targeted user, etc. In order for the search to work for your environment please update
yourdomain.com value in the query with the domain relavant for your organization.
Known False Positives
This search will also produce normal activity statistics. Fields such as email, ip address, name, parameters.organizer_calendar_id, parameters.target_calendar_id and parameters.event_title may give away phishing intent.For more specific results use email parameter.
Associated Analytic Story
The Risk Score is calculated by the following formula: Risk Score = (Impact * Confidence/100). Initial Confidence and Impact is set by the analytic author.
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replay.py tool or the UI.
Alternatively you can replay a dataset into a Splunk Attack Range
source | version: 1