Apoorva Joshi
39 lines
1.9 KiB
TOML
39 lines
1.9 KiB
TOML
[metadata]
|
|
creation_date = "2020/03/25"
|
|
maturity = "production"
|
|
updated_date = "2021/05/10"
|
|
|
|
[rule]
|
|
anomaly_threshold = 50
|
|
author = ["Elastic"]
|
|
description = """
|
|
Searches for rare processes running on multiple Linux hosts in an entire fleet or network. This reduces the detection of
|
|
false positives since automated maintenance processes usually only run occasionally on a single machine but are common
|
|
to all or many hosts in a fleet.
|
|
"""
|
|
false_positives = [
|
|
"""
|
|
A newly installed program or one that runs rarely as part of a monthly or quarterly workflow could trigger this
|
|
alert.
|
|
""",
|
|
]
|
|
from = "now-45m"
|
|
interval = "15m"
|
|
license = "Elastic License v2"
|
|
machine_learning_job_id = ["linux_anomalous_process_all_hosts_ecs", "v2_linux_anomalous_process_all_hosts_ecs"]
|
|
name = "Anomalous Process For a Linux Population"
|
|
note = """## Triage and analysis
|
|
|
|
### Investigating an Unusual Linux Process
|
|
Detection alerts from this rule indicate the presence of a Linux process that is rare and unusual for all of the monitored Linux hosts for which Auditbeat data is available. Here are some possible avenues of investigation:
|
|
- Consider the user as identified by the username field. Is this program part of an expected workflow for the user who ran this program on this host?
|
|
- Examine the history of execution. If this process manifested only very 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 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 = "647fc812-7996-4795-8869-9c4ea595fe88"
|
|
severity = "low"
|
|
tags = ["Elastic", "Host", "Linux", "Threat Detection", "ML"]
|
|
type = "machine_learning"
|
|
|