Terrance DeJesus
e8c39d19a7
* initial commit with eggshell mitre mapping added * adding updated rules * [Rule Tuning] MITRE for GCP rules I've added Mitre references for the 4 GCP rules missing. Changed 3 of the rules from "Impact" to "Defense Evasion" based on the technique used and it's matched tactic. * [Rule Tuning] Endgame Rule name updates for Mitre Updated Endgame rule names for those with Mitre tactics to match the tactics. * Update rules/integrations/aws/persistence_redshift_instance_creation.toml Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com> * Update rules/integrations/aws/exfiltration_rds_snapshot_restored.toml Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com> * adding 10 updated rules for google_workspace, ml and o365 * adding 22 rule updates for mitre att&ck mappings * adding 24 rule updates related mainly to ML rules * adding 3 rules related to detection via ML * adding adjustments * adding adjustments with solutions to recent pytest errors * removed tabs from tags * adjusted mappings and added techniques * adjusted endgame rule mappings per review * adjusted names to match different tactics * added execution and defense evasion tag * adjustments to address errors from merging with main * added newlines to rules missing them at the end of the file Co-authored-by: imays11 <59296946+imays11@users.noreply.github.com> Co-authored-by: Jonhnathan <jonhnathancesar@gmail.com>
59 lines
2.9 KiB
TOML
59 lines
2.9 KiB
TOML
[metadata]
|
|
creation_date = "2020/03/25"
|
|
maturity = "production"
|
|
updated_date = "2022/07/18"
|
|
min_stack_comments = "Supports latest version of ML job introduced in 8.3"
|
|
min_stack_version = "8.3.0"
|
|
|
|
[rule]
|
|
anomaly_threshold = 50
|
|
author = ["Elastic"]
|
|
description = """
|
|
A machine learning job detected activity for a username that is not normally active, which can indicate unauthorized
|
|
changes, activity by unauthorized users, lateral movement, or compromised credentials. In many organizations, new
|
|
usernames are not often created apart from specific types of system activities, such as creating new accounts for new
|
|
employees. These user accounts quickly become active and routine. Events from rarely used usernames can point to
|
|
suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when
|
|
personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for
|
|
malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move
|
|
laterally from one host to another.
|
|
"""
|
|
false_positives = [
|
|
"""
|
|
Uncommon user activity can be due to an engineer logging onto a server instance in order to perform manual
|
|
troubleshooting or reconfiguration.
|
|
""",
|
|
]
|
|
from = "now-45m"
|
|
interval = "15m"
|
|
license = "Elastic License v2"
|
|
machine_learning_job_id = ["v3_linux_anomalous_user_name"]
|
|
name = "Unusual Linux Username"
|
|
note = """## Triage and analysis
|
|
|
|
### Investigating an Unusual Linux User
|
|
Detection alerts from this rule indicate activity for a Linux user name that is rare and unusual. 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? Could this be related to troubleshooting or debugging activity by a developer or site reliability engineer?
|
|
- Examine the history of user activity. If this user only manifested recently, it might be a service account for 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 that the user is performing."""
|
|
references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
|
|
risk_score = 21
|
|
rule_id = "b347b919-665f-4aac-b9e8-68369bf2340c"
|
|
severity = "low"
|
|
tags = ["Elastic", "Host", "Linux", "Threat Detection", "ML", "Initial Access"]
|
|
type = "machine_learning"
|
|
|
|
[[rule.threat]]
|
|
framework = "MITRE ATT&CK"
|
|
[[rule.threat.technique]]
|
|
id = "T1078"
|
|
name = "Valid Accounts"
|
|
reference = "https://attack.mitre.org/techniques/T1078/"
|
|
|
|
|
|
[rule.threat.tactic]
|
|
id = "TA0001"
|
|
name = "Initial Access"
|
|
reference = "https://attack.mitre.org/tactics/TA0001/"
|
|
|