Unusual Windows Network Activity

calendar Aug 22, 2023 · Domain: Endpoint OS: Windows Use Case: Threat Detection Rule Type: ML Rule Type: Machine Learning  ·
Share on: linkedin copy

Identifies Windows processes that do not usually use the network but have unexpected network activity, which can indicate command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network applications.

Elastic rule (View on GitHub)

 1[metadata]
 2creation_date = "2020/03/25"
 3integration = ["endpoint", "windows"]
 4maturity = "production"
 5updated_date = "2023/07/27"
 6min_stack_comments = "New fields added: required_fields, related_integrations, setup"
 7min_stack_version = "8.3.0"
 8
 9[rule]
10anomaly_threshold = 50
11author = ["Elastic"]
12description = """
13Identifies Windows processes that do not usually use the network but have unexpected network activity, which can
14indicate command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual
15network activity can denote process exploitation or injection, where the process is used to run persistence mechanisms
16that allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized
17network applications.
18"""
19false_positives = ["A newly installed program or one that rarely uses the network could trigger this alert."]
20from = "now-45m"
21interval = "15m"
22license = "Elastic License v2"
23machine_learning_job_id = ["v3_windows_anomalous_network_activity"]
24name = "Unusual Windows Network Activity"
25note = """## Triage and analysis
26
27### Investigating Unusual Network Activity
28Detection alerts from this rule indicate the presence of network activity from a Windows process for which network activity is very unusual.  Here are some possible avenues of investigation:
29- Consider the IP addresses, protocol and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected?
30- If the destination IP address is remote or external, does it associate with an expected domain, organization or geography? Note: avoid interacting directly with suspected malicious IP addresses.
31- Consider the user as identified by the username field. Is this network activity part of an expected workflow for the user who ran the program?
32- Examine the history of execution. If this process only manifested recently, it might be part of a new software package. If it has a consistent cadence (for example if it runs monthly or quarterly), it might be part of a monthly or quarterly business process.
33- Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.
34- Consider the same for the parent process. 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.
35- If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools."""
36references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
37risk_score = 21
38rule_id = "ba342eb2-583c-439f-b04d-1fdd7c1417cc"
39severity = "low"
40tags = ["Domain: Endpoint", "OS: Windows", "Use Case: Threat Detection", "Rule Type: ML", "Rule Type: Machine Learning", ]
41type = "machine_learning"

Triage and analysis

Investigating Unusual Network Activity

Detection alerts from this rule indicate the presence of network activity from a Windows process for which network activity is very unusual. Here are some possible avenues of investigation:

  • Consider the IP addresses, protocol and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected?
  • If the destination IP address is remote or external, does it associate with an expected domain, organization or geography? Note: avoid interacting directly with suspected malicious IP addresses.
  • Consider the user as identified by the username field. Is this network activity part of an expected workflow for the user who ran the program?
  • Examine the history of execution. If this process only manifested recently, it might be part of a new software package. If it has a consistent cadence (for example if it runs monthly or quarterly), it might be part of a monthly or quarterly business process.
  • Examine the process arguments, title and working directory. These may provide indications as to the source of the program or the nature of the tasks it is performing.
  • Consider the same for the parent process. 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.
  • If you have file hash values in the event data, and you suspect malware, you can optionally run a search for the file hash to see if the file is identified as malware by anti-malware tools.

References

Related rules

to-top