Files
sigma-rules/rules/ml/ml_windows_anomalous_service.toml
T
Ross Wolf 5fcece8416 Populate rules/ directory.
Co-Authored-By: Brent Murphy <56412096+bm11100@users.noreply.github.com>
Co-Authored-By: Craig Chamberlain <randomuserid@users.noreply.github.com>
Co-Authored-By: David French <56409778+threat-punter@users.noreply.github.com>
Co-Authored-By: Derek Ditch <dcode@users.noreply.github.com>
Co-Authored-By: Justin Ibarra <brokensound77@users.noreply.github.com>
2020-06-29 22:57:03 -06:00

33 lines
1.1 KiB
TOML

[metadata]
creation_date = "2020/03/25"
ecs_version = ["1.5.0"]
maturity = "production"
updated_date = "2020/03/25"
[rule]
anomaly_threshold = 50
author = ["Elastic"]
description = """
A machine learning job detected an unusual Windows service, This can indicate execution of unauthorized services,
malware, or persistence mechanisms. In corporate Windows environments, hosts do not generally run many rare or unique
services. This job helps detect malware and persistence mechanisms that have been installed and run as a service.
"""
false_positives = [
"""
A newly installed program or one that runs rarely as part of a monthly or quarterly workflow could trigger this
signal.
""",
]
from = "now-45m"
interval = "15m"
license = "Elastic License"
machine_learning_job_id = "windows_anomalous_service"
name = "Unusual Windows Service"
references = ["https://www.elastic.co/guide/en/siem/guide/current/prebuilt-ml-jobs.html"]
risk_score = 21
rule_id = "1781d055-5c66-4adf-9c71-fc0fa58338c7"
severity = "low"
tags = ["Elastic", "ML", "Windows"]
type = "machine_learning"