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sigma-rules/rules/ml/ml_linux_anomalous_network_activity.toml
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Jonhnathan 38b8311482 [Security Content] Expand Abbreviated Tags (#2414)
* [Security Content] Expand Abbreviated Tags

* .

* Update privilege_escalation_gcp_kubernetes_rolebindings_created_or_patched.toml

* Apply suggestions from code review

Co-authored-by: Isai <59296946+imays11@users.noreply.github.com>

* Revert changes to deprecated rules

* Bump updated_date

---------

Co-authored-by: Isai <59296946+imays11@users.noreply.github.com>
2023-03-06 17:37:52 -03:00

39 lines
2.3 KiB
TOML

[metadata]
creation_date = "2020/03/25"
maturity = "production"
updated_date = "2023/03/06"
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 = ["Elastic", "Host", "Linux", "Threat Detection", "ML", "Machine Learning", ]
type = "machine_learning"