Files
sigma-rules/rules/ml/ml_linux_anomalous_network_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

27 lines
841 B
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 = """
Identifies unusual listening ports on Linux instances that can indicate execution of unauthorized services, backdoors,
or persistence mechanisms.
"""
false_positives = ["A newly installed program or one that rarely uses the network could trigger this signal."]
from = "now-45m"
interval = "15m"
license = "Elastic License"
machine_learning_job_id = "linux_anomalous_network_service"
name = "Unusual Linux Network Service"
references = ["https://www.elastic.co/guide/en/siem/guide/current/prebuilt-ml-jobs.html"]
risk_score = 21
rule_id = "52afbdc5-db15-596e-bc35-f5707f820c4b"
severity = "low"
tags = ["Elastic", "Linux", "ML"]
type = "machine_learning"