Files
sigma-rules/rules/ml/ml_linux_anomalous_metadata_user.toml
T
Bobby Filar 9a739b7e4c Modifying rules assoc w/ deprecation of v2 ML jobs (#1846)
* modifying rules assoc w/ deprecation of v2 ML jobs

* modified updated_date field

* fixed machine_learning_job_id and added min_stack_version

* replacing rest of deprecated jobs with new naming convention

* Update ml_suspicious_login_activity.toml

* removing rules assoc w/ deprecated ML jobs

* Update rules/ml/ml_linux_anomalous_compiler_activity.toml

Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com>

* Update rules/ml/ml_linux_anomalous_compiler_activity.toml

Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com>

* updated ml job rules to reflect 8.3 changes

* updating min_stack_version for ml detection rules

Co-authored-by: Craig Chamberlain <randomuserid@users.noreply.github.com>
Co-authored-by: Justin Ibarra <brokensound77@users.noreply.github.com>
Co-authored-by: Apoorva Joshi <30438249+ajosh0504@users.noreply.github.com>
2022-05-20 13:02:27 -07:00

32 lines
1.1 KiB
TOML

[metadata]
creation_date = "2020/09/22"
maturity = "production"
updated_date = "2022/05/12"
min_stack_comments = "Supports latest version of ML job introduced in 8.3"
min_stack_version = "8.3.0"
[rule]
anomaly_threshold = 75
author = ["Elastic"]
description = """
Looks for anomalous access to the cloud platform metadata service by an unusual user. The metadata service may be
targeted in order to harvest credentials or user data scripts containing secrets.
"""
false_positives = [
"""
A newly installed program, or one that runs under a new or rarely used user context, could trigger this detection
rule. Manual interrogation of the metadata service during debugging or troubleshooting could trigger this rule.
""",
]
from = "now-45m"
interval = "15m"
license = "Elastic License v2"
machine_learning_job_id = ["v3_linux_rare_metadata_user"]
name = "Unusual Linux User Calling the Metadata Service"
risk_score = 21
rule_id = "1faec04b-d902-4f89-8aff-92cd9043c16f"
severity = "low"
tags = ["Elastic", "Host", "Linux", "Threat Detection", "ML"]
type = "machine_learning"