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Description

This analytics is to detect a gmail containing a link that are known to be abused by malware or attacker like pastebin, telegram and discord to deliver malicious payload. This event can encounter some normal email traffic within organization and external email that normally using this application and services.

  • Type: Anomaly

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

  • Last Updated: 2021-08-23
  • Author: Teoderick Contreras, Splunk
  • ID: 8630aa22-042b-11ec-af39-acde48001122

Annotations

ATT&CK

ATT&CK

ID Technique Tactic
T1566.001 Spearphishing Attachment Initial Access
T1566 Phishing Initial Access
Kill Chain Phase
  • Exploitation
NIST
CIS20
CVE 


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`gsuite_gmail` "link_domain{}" IN ("*pastebin.com*", "*discord*", "*telegram*","t.me") 
| rex field=source.from_header_address "[^@]+@(?<source_domain>[^@]+)" 
| rex field=destination{}.address "[^@]+@(?<dest_domain>[^@]+)" 
| where not source_domain="internal_test_email.com" and dest_domain="internal_test_email.com" 
| eval phase="plan" 
| eval severity="low" 
|stats values(link_domain{}) as link_domains min(_time) as firstTime max(_time) as lastTime count by is_spam source.address source.from_header_address subject destination{}.address phase severity 
| `security_content_ctime(firstTime)` 
| `security_content_ctime(lastTime)` 
| `gsuite_email_with_known_abuse_web_service_link_filter`

Macros

The SPL above uses the following Macros:

:information_source: gsuite_email_with_known_abuse_web_service_link_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.

  • _time

How To Implement

To successfully implement this search, you need to be ingesting logs related to gsuite having the file attachment metadata like file type, file extension, source email, destination email, num of attachment and etc.

Known False Positives

normal email contains this link that are known application within the organization or network can be catched by this detection.

Associated Analytic Story

RBA

Risk Score Impact Confidence Message
25.0 50 50 suspicious email from $source.address$ to $destination{}.address$

: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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