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persistence_ml_windows_anomalous_process_creation.toml
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persistence_ml_windows_anomalous_process_creation.toml
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[metadata]
creation_date = "2020/03/25"
integration = ["endpoint", "windows"]
maturity = "production"
updated_date = "2024/06/18"
[transform]
[[transform.osquery]]
label = "Osquery - Retrieve DNS Cache"
query = "SELECT * FROM dns_cache"
[[transform.osquery]]
label = "Osquery - Retrieve All Services"
query = "SELECT description, display_name, name, path, pid, service_type, start_type, status, user_account FROM services"
[[transform.osquery]]
label = "Osquery - Retrieve Services Running on User Accounts"
query = """
SELECT description, display_name, name, path, pid, service_type, start_type, status, user_account FROM services WHERE
NOT (user_account LIKE '%LocalSystem' OR user_account LIKE '%LocalService' OR user_account LIKE '%NetworkService' OR
user_account == null)
"""
[[transform.osquery]]
label = "Retrieve Service Unisgned Executables with Virustotal Link"
query = """
SELECT concat('https://www.virustotal.com/gui/file/', sha1) AS VtLink, name, description, start_type, status, pid,
services.path FROM services JOIN authenticode ON services.path = authenticode.path OR services.module_path =
authenticode.path JOIN hash ON services.path = hash.path WHERE authenticode.result != 'trusted'
"""
[rule]
anomaly_threshold = 50
author = ["Elastic"]
description = """
Identifies unusual parent-child process relationships that can indicate malware execution or persistence mechanisms.
Malicious scripts often call on other applications and processes as part of their exploit payload. For example, when a
malicious Office document runs scripts as part of an exploit payload, Excel or Word may start a script interpreter
process, which, in turn, runs a script that downloads and executes malware. Another common scenario is Outlook running
an unusual process when malware is downloaded in an email. Monitoring and identifying anomalous process relationships is
a method of detecting new and emerging malware that is not yet recognized by anti-virus scanners.
"""
false_positives = [
"""
Users running scripts in the course of technical support operations of software upgrades could trigger this alert. A
newly installed program or one that runs rarely as part of a monthly or quarterly workflow could trigger this alert.
""",
]
from = "now-45m"
interval = "15m"
license = "Elastic License v2"
machine_learning_job_id = ["v3_windows_anomalous_process_creation"]
name = "Anomalous Windows Process Creation"
setup = """## Setup
This rule requires the installation of associated Machine Learning jobs, as well as data coming in from one of the following integrations:
- Elastic Defend
- Windows
### Anomaly Detection Setup
Once the rule is enabled, the associated Machine Learning job will start automatically. You can view the Machine Learning job linked under the "Definition" panel of the detection rule. If the job does not start due to an error, the issue must be resolved for the job to commence successfully. For more details on setting up anomaly detection jobs, refer to the [helper guide](https://www.elastic.co/guide/en/kibana/current/xpack-ml-anomalies.html).
### Elastic Defend Integration Setup
Elastic Defend is integrated into the Elastic Agent using Fleet. Upon configuration, the integration allows the Elastic Agent to monitor events on your host and send data to the Elastic Security app.
#### Prerequisite Requirements:
- Fleet is required for Elastic Defend.
- To configure Fleet Server refer to the [documentation](https://www.elastic.co/guide/en/fleet/current/fleet-server.html).
#### The following steps should be executed in order to add the Elastic Defend integration to your system:
- Go to the Kibana home page and click "Add integrations".
- In the query bar, search for "Elastic Defend" and select the integration to see more details about it.
- Click "Add Elastic Defend".
- Configure the integration name and optionally add a description.
- Select the type of environment you want to protect, either "Traditional Endpoints" or "Cloud Workloads".
- Select a configuration preset. Each preset comes with different default settings for Elastic Agent, you can further customize these later by configuring the Elastic Defend integration policy. [Helper guide](https://www.elastic.co/guide/en/security/current/configure-endpoint-integration-policy.html).
- We suggest selecting "Complete EDR (Endpoint Detection and Response)" as a configuration setting, that provides "All events; all preventions"
- Enter a name for the agent policy in "New agent policy name". If other agent policies already exist, you can click the "Existing hosts" tab and select an existing policy instead.
For more details on Elastic Agent configuration settings, refer to the [helper guide](https://www.elastic.co/guide/en/fleet/current/agent-policy.html).
- Click "Save and Continue".
- To complete the integration, select "Add Elastic Agent to your hosts" and continue to the next section to install the Elastic Agent on your hosts.
For more details on Elastic Defend refer to the [helper guide](https://www.elastic.co/guide/en/security/current/install-endpoint.html).
### Windows Integration Setup
The Windows integration allows you to monitor the Windows OS, services, applications, and more.
#### The following steps should be executed in order to add the Elastic Agent System integration "windows" to your system:
- Go to the Kibana home page and click “Add integrations”.
- In the query bar, search for “Windows” and select the integration to see more details about it.
- Click “Add Windows”.
- Configure the integration name and optionally add a description.
- Review optional and advanced settings accordingly.
- Add the newly installed “windows” to an existing or a new agent policy, and deploy the agent on your system from which windows log files are desirable.
- Click “Save and Continue”.
- For more details on the integration refer to the [helper guide](https://docs.elastic.co/integrations/windows).
"""
note = """## Triage and analysis
### Investigating Anomalous Windows Process Creation
Searching for abnormal Windows processes is a good methodology to find potentially malicious activity within a network. Understanding what is commonly run within an environment and developing baselines for legitimate activity can help uncover potential malware and suspicious behaviors.
This rule uses a machine learning job to detect an anomalous Windows process with an unusual parent-child relationship, which could indicate malware execution or persistence activities on the host machine.
> **Note**:
> This investigation guide uses the [Osquery Markdown Plugin](https://www.elastic.co/guide/en/security/master/invest-guide-run-osquery.html) introduced in Elastic Stack version 8.5.0. Older Elastic Stack versions will display unrendered Markdown in this guide.
#### Possible investigation steps
- Investigate the process execution chain (parent process tree) for unknown processes. Examine their executable files for prevalence, whether they are located in expected locations, and if they are signed with valid digital signatures.
- If the parent process is a legitimate system utility or service, this could be related to software updates or system management. If the parent process is something user-facing like an Office application, this process could be more suspicious.
- Investigate the process metadata — such as the digital signature, directory, etc. — to obtain more context that can indicate whether the executable is associated with an expected software vendor or package.
- Investigate other alerts associated with the user/host during the past 48 hours.
- Consider the user as identified by the `user.name` field. Is this program part of an expected workflow for the user who ran this program on this host?
- Validate the activity is not related to planned patches, updates, network administrator activity, or legitimate software installations.
- Validate if the activity has a consistent cadence (for example, if it runs monthly or quarterly), as it could be part of a monthly or quarterly business process.
- Examine the arguments and working directory of the process. These may provide indications as to the source of the program or the nature of the tasks it is performing.
- Examine the host for derived artifacts that indicate suspicious activities:
- Analyze the process executable using a private sandboxed analysis system.
- Observe and collect information about the following activities in both the sandbox and the alert subject host:
- Attempts to contact external domains and addresses.
- Use the Elastic Defend network events to determine domains and addresses contacted by the subject process by filtering by the process' `process.entity_id`.
- Examine the DNS cache for suspicious or anomalous entries.
- $osquery_0
- Use the Elastic Defend registry events to examine registry keys accessed, modified, or created by the related processes in the process tree.
- Examine the host services for suspicious or anomalous entries.
- $osquery_1
- $osquery_2
- $osquery_3
- Retrieve the files' SHA-256 hash values using the PowerShell `Get-FileHash` cmdlet and search for the existence and reputation of the hashes in resources like VirusTotal, Hybrid-Analysis, CISCO Talos, Any.run, etc.
### False Positive Analysis
- If this activity is related to new benign software installation activity, consider adding exceptions — preferably with a combination of user and command line conditions.
- Try to understand the context of the execution by thinking about the user, machine, or business purpose. A small number of endpoints, such as servers with unique software, might appear unusual but satisfy a specific business need.
### Related Rules
- Unusual Process For a Windows Host - 6d448b96-c922-4adb-b51c-b767f1ea5b76
- Unusual Windows Path Activity - 445a342e-03fb-42d0-8656-0367eb2dead5
- Unusual Windows Process Calling the Metadata Service - abae61a8-c560-4dbd-acca-1e1438bff36b
### Response and Remediation
- Initiate the incident response process based on the outcome of the triage.
- Isolate the involved hosts to prevent further post-compromise behavior.
- If the triage identified malware, search the environment for additional compromised hosts.
- Implement temporary network rules, procedures, and segmentation to contain the malware.
- Stop suspicious processes.
- Immediately block the identified indicators of compromise (IoCs).
- Inspect the affected systems for additional malware backdoors like reverse shells, reverse proxies, or droppers that attackers could use to reinfect the system.
- Remove and block malicious artifacts identified during triage.
- Investigate credential exposure on systems compromised or used by the attacker to ensure all compromised accounts are identified. Reset passwords for these accounts and other potentially compromised credentials, such as email, business systems, and web services.
- Run a full antimalware scan. This may reveal additional artifacts left in the system, persistence mechanisms, and malware components.
- Determine the initial vector abused by the attacker and take action to prevent reinfection through the same vector.
- Using the incident response data, update logging and audit policies to improve the mean time to detect (MTTD) and the mean time to respond (MTTR).
"""
references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
risk_score = 21
rule_id = "0b29cab4-dbbd-4a3f-9e8e-1287c7c11ae5"
severity = "low"
tags = [
"Domain: Endpoint",
"OS: Windows",
"Use Case: Threat Detection",
"Rule Type: ML",
"Rule Type: Machine Learning",
"Tactic: Persistence",
"Resources: Investigation Guide",
]
type = "machine_learning"
[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1543"
name = "Create or Modify System Process"
reference = "https://attack.mitre.org/techniques/T1543/"
[rule.threat.tactic]
id = "TA0003"
name = "Persistence"
reference = "https://attack.mitre.org/tactics/TA0003/"