Unusual Linux Username

 1[metadata]
 2creation_date = "2020/03/25"
 3integration = ["auditd_manager", "endpoint"]
 4maturity = "production"
 5updated_date = "2023/07/27"
 6min_stack_comments = "New fields added: required_fields, related_integrations, setup"
 7min_stack_version = "8.3.0"
 8
 9[rule]
10anomaly_threshold = 50
11author = ["Elastic"]
12description = """
13A machine learning job detected activity for a username that is not normally active, which can indicate unauthorized
14changes, activity by unauthorized users, lateral movement, or compromised credentials. In many organizations, new
15usernames are not often created apart from specific types of system activities, such as creating new accounts for new
16employees. These user accounts quickly become active and routine. Events from rarely used usernames can point to
17suspicious activity. Additionally, automated Linux fleets tend to see activity from rarely used usernames only when
18personnel log in to make authorized or unauthorized changes, or threat actors have acquired credentials and log in for
19malicious purposes. Unusual usernames can also indicate pivoting, where compromised credentials are used to try and move
20laterally from one host to another.
21"""
22false_positives = [
23    """
24    Uncommon user activity can be due to an engineer logging onto a server instance in order to perform manual
25    troubleshooting or reconfiguration.
26    """,
27]
28from = "now-45m"
29interval = "15m"
30license = "Elastic License v2"
31machine_learning_job_id = ["v3_linux_anomalous_user_name"]
32name = "Unusual Linux Username"
33note = """## Triage and analysis
34
35### Investigating an Unusual Linux User
36Detection alerts from this rule indicate activity for a Linux user name that is rare and unusual. Here are some possible avenues of investigation:
37- 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?
38- 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.
39- 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."""
40references = ["https://www.elastic.co/guide/en/security/current/prebuilt-ml-jobs.html"]
41risk_score = 21
42rule_id = "b347b919-665f-4aac-b9e8-68369bf2340c"
43severity = "low"
44tags = ["Domain: Endpoint", "OS: Linux", "Use Case: Threat Detection", "Rule Type: ML", "Rule Type: Machine Learning", "Tactic: Initial Access"]
45type = "machine_learning"
46
47[[rule.threat]]
48framework = "MITRE ATT&CK"
49[[rule.threat.technique]]
50id = "T1078"
51name = "Valid Accounts"
52reference = "https://attack.mitre.org/techniques/T1078/"
53
54
55[rule.threat.tactic]
56id = "TA0001"
57name = "Initial Access"
58reference = "https://attack.mitre.org/tactics/TA0001/"