---
title: "Suspicious Email - UBA Anomaly"
excerpt: "Phishing
"
categories:
  - Deprecated
last_modified_at: 2020-07-22
toc: true
toc_label: ""
tags:
  - Phishing
  - Initial Access
  - Splunk Enterprise
  - Splunk Enterprise Security
  - Splunk Cloud
  - UEBA
---



[Try in Splunk Security Cloud](https://www.splunk.com/en_us/cyber-security.html){: .btn .btn--success}

#### Description

This detection looks for emails that are suspicious because of their sender, domain rareness, or behavior differences. This is an anomaly generated by Splunk User Behavior Analytics (UBA).

- **Type**: [Anomaly](https://github.com/splunk/security_content/wiki/object-Analytic-Types)
- **Product**: Splunk Enterprise, Splunk Enterprise Security, Splunk Cloud
- **Datamodel**: [UEBA](https://docs.splunk.com/Documentation/CIM/latest/User/UEBA)
- **Last Updated**: 2020-07-22
- **Author**: Bhavin Patel, Splunk
- **ID**: 56e877a6-1455-4479-ad16-0550dc1e33f8


#### [ATT&CK](https://attack.mitre.org/)

| ID             | Technique        |  Tactic             |
| -------------- | ---------------- |-------------------- |
| [T1566](https://attack.mitre.org/techniques/T1566/) | Phishing | Initial Access |

#### Search

```

|tstats `security_content_summariesonly` count min(_time) as firstTime max(_time) as lastTime values(All_UEBA_Events.category) as category from datamodel=UEBA where nodename=All_UEBA_Events.UEBA_Anomalies All_UEBA_Events.UEBA_Anomalies.uba_model = "SuspiciousEmailDetectionModel" by All_UEBA_Events.description All_UEBA_Events.severity All_UEBA_Events.user All_UEBA_Events.uba_event_type All_UEBA_Events.link All_UEBA_Events.signature All_UEBA_Events.url All_UEBA_Events.UEBA_Anomalies.uba_model 
| `drop_dm_object_name(All_UEBA_Events)` 
| `drop_dm_object_name(UEBA_Anomalies)`
| `security_content_ctime(firstTime)`
| `security_content_ctime(lastTime)` 
| `suspicious_email___uba_anomaly_filter`
```

#### Macros
The SPL above uses the following Macros:
* [security_content_ctime](https://github.com/splunk/security_content/blob/develop/macros/security_content_ctime.yml)
* [security_content_summariesonly](https://github.com/splunk/security_content/blob/develop/macros/security_content_summariesonly.yml)

Note that `suspicious_email_-_uba_anomaly_filter` is a empty macro by default. It allows the user to filter out any results (false positives) without editing the SPL.

#### Required field
* _time


#### How To Implement
You must be ingesting data from email logs and have Splunk integrated with UBA. This anomaly is raised by a UBA detection model called  "SuspiciousEmailDetectionModel." Ensure that this model is enabled on your UBA instance.

#### Known False Positives
This detection model will alert on any sender domain that is seen for the first time. This could be a potential false positive. The next step is to investigate and add the URL to an allow list if you determine that it is a legitimate sender.

#### Associated Analytic story
* [Suspicious Emails](/stories/suspicious_emails)


#### Kill Chain Phase
* Delivery



#### RBA

| Risk Score  | Impact      | Confidence   | Message      |
| ----------- | ----------- |--------------|--------------|
| 25.0 | 50 | 50 | tbd |




#### Reference


#### Test Dataset
Replay any dataset to Splunk Enterprise by using our [`replay.py`](https://github.com/splunk/attack_data#using-replaypy) tool or the [UI](https://github.com/splunk/attack_data#using-ui).
Alternatively you can replay a dataset into a [Splunk Attack Range](https://github.com/splunk/attack_range#replay-dumps-into-attack-range-splunk-server)



[*source*](https://github.com/splunk/security_content/tree/develop/detections/deprecated/suspicious_email_-_uba_anomaly.yml) \| *version*: **3**