Unusual Linux Network Activity

Identifies Linux 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 = ["auditd_manager", "endpoint"]
 4maturity = "production"
 5updated_date = "2024/05/21"
 6
 7[rule]
 8anomaly_threshold = 50
 9author = ["Elastic"]
10description = """
11Identifies Linux processes that do not usually use the network but have unexpected network activity, which can indicate
12command-and-control, lateral movement, persistence, or data exfiltration activity. A process with unusual network
13activity can denote process exploitation or injection, where the process is used to run persistence mechanisms that
14allow a malicious actor remote access or control of the host, data exfiltration, and execution of unauthorized network
15applications.
16"""
17from = "now-45m"
18interval = "15m"
19license = "Elastic License v2"
20machine_learning_job_id = ["v3_linux_anomalous_network_activity"]
21name = "Unusual Linux Network Activity"
22note = """## Triage and analysis
23
24### Investigating Unusual Network Activity
25Detection alerts from this rule indicate the presence of network activity from a Linux process for which network activity is rare and unusual.  Here are some possible avenues of investigation:
26- Consider the IP addresses and ports. Are these used by normal but infrequent network workflows? Are they expected or unexpected?
27- 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.
28- 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?
29- 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 or maintenance process.
30- 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."""
31references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
32risk_score = 21
33rule_id = "52afbdc5-db15-485e-bc24-f5707f820c4b"
34severity = "low"
35tags = [
36    "Domain: Endpoint",
37    "OS: Linux",
38    "Use Case: Threat Detection",
39    "Rule Type: ML",
40    "Rule Type: Machine Learning",
41]
42type = "machine_learning"

Triage and analysis

Investigating Unusual Network Activity

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

  • Consider the IP addresses 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 or maintenance 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.

References

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