Machine Learning Detected a Suspicious Windows Event with a Low Malicious Probability Score

edit

Machine Learning Detected a Suspicious Windows Event with a Low Malicious Probability Score

edit

A supervised machine learning model (ProblemChild) has identified a suspicious Windows process event with low probability of it being malicious activity. Alternatively, the model’s blocklist identified the event as being malicious.

Rule type: eql

Rule indices:

endgame-*
logs-endpoint.events.process-*
winlogbeat-*

Severity: low

Risk score: 21

Runs every: 5m

Searches indices from: now-10m (Date Math format, see also Additional look-back time)

Maximum alerts per execution: 100

References:

Tags:

OS: Windows
Data Source: Elastic Endgame
Use Case: Living off the Land Attack Detection
Rule Type: ML
Rule Type: Machine Learning
Tactic: Defense Evasion
Data Source: Elastic Defend

Version: 8

Rule authors:

Elastic

Rule license: Elastic License v2

Setup

edit

Setup

The rule requires the Living off the Land (LotL) Attack Detection integration assets to be installed, as well as Windows process events collected by integrations such as Elastic Defend or Winlogbeat.

LotL Attack Detection Setup

The LotL Attack Detection integration detects living-off-the-land activity in Windows process events.

Prerequisite Requirements:

Fleet is required for LotL Attack Detection.
To configure Fleet Server refer to the documentation.
Windows process events collected by the Elastic Defend integration or Winlogbeat(https://www.elastic.co/guide/en/beats/winlogbeat/current/_winlogbeat_overview.html).
To install Elastic Defend, refer to the documentation.
To set up and run Winlogbeat, follow this guide.

The following steps should be executed to install assets associated with the LotL Attack Detection integration:

Go to the Kibana homepage. Under Management, click Integrations.
In the query bar, search for Living off the Land Attack Detection and select the integration to see more details about it.
Follow the instructions under the Installation section.
For this rule to work, complete the instructions through Configure the ingest pipeline.

Rule query

edit

process where ((problemchild.prediction == 1 and problemchild.prediction_probability <= 0.98) or
blocklist_label == 1) and not process.args : ("*C:\\WINDOWS\\temp\\nessus_*.txt*", "*C:\\WINDOWS\\temp\\nessus_*.tmp*")

Framework: MITRE ATT&CK^TM

Tactic:
- Name: Defense Evasion
- ID: TA0005
- Reference URL: https://attack.mitre.org/tactics/TA0005/
Technique:
- Name: Masquerading
- ID: T1036
- Reference URL: https://attack.mitre.org/techniques/T1036/
Sub-technique:
- Name: Masquerade Task or Service
- ID: T1036.004
- Reference URL: https://attack.mitre.org/techniques/T1036/004/

« Machine Learning Detected a Suspicious Windows Event with a High Malicious Probability Score Malware - Detected - Elastic Endgame »