Unusual Network Destination Domain Name

A machine learning job detected an unusual network destination domain name. This can be due to initial access, persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing email or opens a malicious document, a request may be sent to download and run a payload from an uncommon web server name. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for command-and-control communication.

Elastic rule (View on GitHub)

 1[metadata]
 2creation_date = "2020/03/25"
 3integration = ["auditd_manager", "endpoint"]
 4maturity = "production"
 5updated_date = "2024/06/18"
 6
 7[rule]
 8anomaly_threshold = 50
 9author = ["Elastic"]
10description = """
11A machine learning job detected an unusual network destination domain name. This can be due to initial access,
12persistence, command-and-control, or exfiltration activity. For example, when a user clicks on a link in a phishing
13email or opens a malicious document, a request may be sent to download and run a payload from an uncommon web server
14name. When malware is already running, it may send requests to an uncommon DNS domain the malware uses for
15command-and-control communication.
16"""
17false_positives = [
18    """
19    Web activity that occurs rarely in small quantities can trigger this alert. Possible examples are browsing technical
20    support or vendor URLs that are used very sparsely. A user who visits a new and unique web destination may trigger
21    this alert when the activity is sparse. Web applications that generate URLs unique to a transaction may trigger this
22    when they are used sparsely. Web domains can be excluded in cases such as these.
23    """,
24]
25from = "now-45m"
26interval = "15m"
27license = "Elastic License v2"
28machine_learning_job_id = "packetbeat_rare_server_domain"
29name = "Unusual Network Destination Domain Name"
30setup = """## Setup
31
32This rule requires the installation of associated Machine Learning jobs, as well as data coming in from one of the following integrations:
33- Elastic Defend
34- Auditd Manager
35
36### Anomaly Detection Setup
37
38Once 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).
39
40### Elastic Defend Integration Setup
41Elastic 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.
42
43#### Prerequisite Requirements:
44- Fleet is required for Elastic Defend.
45- To configure Fleet Server refer to the [documentation](https://www.elastic.co/guide/en/fleet/current/fleet-server.html).
46
47#### The following steps should be executed in order to add the Elastic Defend integration to your system:
48- Go to the Kibana home page and click "Add integrations".
49- In the query bar, search for "Elastic Defend" and select the integration to see more details about it.
50- Click "Add Elastic Defend".
51- Configure the integration name and optionally add a description.
52- Select the type of environment you want to protect, either "Traditional Endpoints" or "Cloud Workloads".
53- 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).
54- We suggest selecting "Complete EDR (Endpoint Detection and Response)" as a configuration setting, that provides "All events; all preventions"
55- 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.
56For more details on Elastic Agent configuration settings, refer to the [helper guide](https://www.elastic.co/guide/en/fleet/current/agent-policy.html).
57- Click "Save and Continue".
58- 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.
59For more details on Elastic Defend refer to the [helper guide](https://www.elastic.co/guide/en/security/current/install-endpoint.html).
60
61### Auditd Manager Integration Setup
62The Auditd Manager Integration receives audit events from the Linux Audit Framework which is a part of the Linux kernel.
63Auditd Manager provides a user-friendly interface and automation capabilities for configuring and monitoring system auditing through the auditd daemon. With `auditd_manager`, administrators can easily define audit rules, track system events, and generate comprehensive audit reports, improving overall security and compliance in the system.
64
65#### The following steps should be executed in order to add the Elastic Agent System integration "auditd_manager" to your system:
66- Go to the Kibana home page and click “Add integrations”.
67- In the query bar, search for “Auditd Manager” and select the integration to see more details about it.
68- Click “Add Auditd Manager”.
69- Configure the integration name and optionally add a description.
70- Review optional and advanced settings accordingly.
71- Add the newly installed “auditd manager” to an existing or a new agent policy, and deploy the agent on a Linux system from which auditd log files are desirable.
72- Click “Save and Continue”.
73- For more details on the integration refer to the [helper guide](https://docs.elastic.co/integrations/auditd_manager).
74
75#### Rule Specific Setup Note
76Auditd Manager subscribes to the kernel and receives events as they occur without any additional configuration.
77However, if more advanced configuration is required to detect specific behavior, audit rules can be added to the integration in either the "audit rules" configuration box or the "auditd rule files" box by specifying a file to read the audit rules from.
78- For this detection rule no additional audit rules are required.
79"""
80references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
81risk_score = 21
82rule_id = "17e68559-b274-4948-ad0b-f8415bb31126"
83severity = "low"
84tags = ["Use Case: Threat Detection", "Rule Type: ML", "Rule Type: Machine Learning"]
85type = "machine_learning"

References

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