[metadata]
creation_date = "2020/03/25"
integration = ["auditd_manager", "endpoint"]
maturity = "production"
updated_date = "2023/07/27"
min_stack_comments = "New fields added: required_fields, related_integrations, setup"
min_stack_version = "8.3.0"

[rule]
anomaly_threshold = 50
author = ["Elastic"]
description = """
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.
"""
from = "now-45m"
interval = "15m"
license = "Elastic License v2"
machine_learning_job_id = ["v3_linux_anomalous_network_activity"]
name = "Unusual Linux Network Activity"
note = """## 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 = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
risk_score = 21
rule_id = "52afbdc5-db15-485e-bc24-f5707f820c4b"
severity = "low"
tags = ["Domain: Endpoint", "OS: Linux", "Use Case: Threat Detection", "Rule Type: ML", "Rule Type: Machine Learning", ]
type = "machine_learning"