ID | Technique | Tactic |
---|
Detection: Email Attachments With Lots Of Spaces
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.
Description
The following analytic detects email attachments with an unusually high number of spaces in their file names, which is a common tactic used by attackers to obfuscate file extensions. It leverages the Email data model to identify attachments where the ratio of spaces to the total file name length exceeds 10%. This behavior is significant as it may indicate an attempt to bypass security filters and deliver malicious payloads. If confirmed malicious, this activity could lead to the execution of harmful code or unauthorized access to sensitive information within the recipient's environment.
Search
1
2| tstats `security_content_summariesonly` count values(All_Email.recipient) as recipient_address min(_time) as firstTime max(_time) as lastTime from datamodel=Email where All_Email.file_name="*" by All_Email.src_user, All_Email.file_name All_Email.message_id
3| `security_content_ctime(firstTime)`
4| `security_content_ctime(lastTime)`
5| `drop_dm_object_name("All_Email")`
6| eval space_ratio = (mvcount(split(file_name," "))-1)/len(file_name)
7| search space_ratio >= 0.1
8| rex field=recipient_address "(?<recipient_user>.*)@"
9| `email_attachments_with_lots_of_spaces_filter`
Data Source
Name | Platform | Sourcetype | Source | Supported App |
---|---|---|---|---|
N/A | N/A | N/A | N/A | N/A |
Macros Used
Name | Value |
---|---|
security_content_ctime | convert timeformat="%Y-%m-%dT%H:%M:%S" ctime($field$) |
email_attachments_with_lots_of_spaces_filter | search * |
email_attachments_with_lots_of_spaces_filter
is an empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.
Annotations
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 |
Implementation
You need to ingest data from emails. Specifically, the sender's address and the file names of any attachments must be mapped to the Email data model. The threshold ratio is set to 10%, but this value can be configured to suit each environment.
Splunk Phantom Playbook Integration
If Splunk Phantom is also configured in your environment, a playbook called "Suspicious Email Attachment Investigate and Delete" can be configured to run when any results are found by this detection search. To use this integration, install the Phantom App for Splunk https://splunkbase.splunk.com/app/3411/
and add the correct hostname to the "Phantom Instance" field in the Adaptive Response Actions when configuring this detection search. The notable event will be sent to Phantom and the playbook will gather further information about the file attachment and its network behaviors. If Phantom finds malicious behavior and an analyst approves of the results, the email will be deleted from the user's inbox.
Known False Positives
None at this time
Associated Analytic Story
Risk Based Analytics (RBA)
Risk Message | Risk Score | Impact | Confidence |
---|---|---|---|
tbd | 25 | 50 | 50 |
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