sigma-rules/rules/windows/credential_access_bruteforce_admin_account.toml

[metadata]
creation_date = "2020/08/29"
integration = ["system", "windows"]
maturity = "production"
min_stack_comments = "New fields added: required_fields, related_integrations, setup"
min_stack_version = "8.3.0"
updated_date = "2023/10/23"

[transform]
[[transform.osquery]]
label = "Osquery - Retrieve DNS Cache"
query = "SELECT * FROM dns_cache"

[[transform.osquery]]
label = "Osquery - Retrieve All Services"
query = "SELECT description, display_name, name, path, pid, service_type, start_type, status, user_account FROM services"

[[transform.osquery]]
label = "Osquery - Retrieve Services Running on User Accounts"
query = """
SELECT description, display_name, name, path, pid, service_type, start_type, status, user_account FROM services WHERE
NOT (user_account LIKE '%LocalSystem' OR user_account LIKE '%LocalService' OR user_account LIKE '%NetworkService' OR
user_account == null)
"""

[[transform.osquery]]
label = "Osquery - Retrieve Service Unsigned Executables with Virustotal Link"
query = """
SELECT concat('https://www.virustotal.com/gui/file/', sha1) AS VtLink, name, description, start_type, status, pid,
services.path FROM services JOIN authenticode ON services.path = authenticode.path OR services.module_path =
authenticode.path JOIN hash ON services.path = hash.path WHERE authenticode.result != 'trusted'
"""


[rule]
author = ["Elastic"]
description = """
Identifies multiple consecutive logon failures targeting an Admin account from the same source address and within a
short time interval. Adversaries will often brute force login attempts across multiple users with a common or known
password, in an attempt to gain access to accounts.
"""
from = "now-9m"
index = ["winlogbeat-*", "logs-system.*", "logs-windows.*"]
language = "eql"
license = "Elastic License v2"
name = "Privileged Account Brute Force"
note = """## Triage and analysis

### Investigating Privileged Account Brute Force

Adversaries with no prior knowledge of legitimate credentials within the system or environment may guess passwords to attempt access to accounts. Without knowledge of the password for an account, an adversary may opt to guess the password using a repetitive or iterative mechanism systematically. More details can be found [here](https://attack.mitre.org/techniques/T1110/001/).

This rule identifies potential password guessing/brute force activity from a single address against an account that contains the `admin` pattern on its name, which is likely a highly privileged account.

> **Note**:
> This investigation guide uses the [Osquery Markdown Plugin](https://www.elastic.co/guide/en/security/master/invest-guide-run-osquery.html) introduced in Elastic Stack version 8.5.0. Older Elastic Stack versions will display unrendered Markdown in this guide.

#### Possible investigation steps

- Investigate the logon failure reason code and the targeted user name.
  - Prioritize the investigation if the account is critical or has administrative privileges over the domain.
- Investigate the source IP address of the failed Network Logon attempts.
  - Identify whether these attempts are coming from the internet or are internal.
- Investigate other alerts associated with the involved users and source host during the past 48 hours.
- Identify the source and the target computer and their roles in the IT environment.
- Check whether the involved credentials are used in automation or scheduled tasks.
- If this activity is suspicious, contact the account owner and confirm whether they are aware of it.
- Examine the source host for derived artifacts that indicate compromise:
  - Observe and collect information about the following activities in the alert source host:
    - Attempts to contact external domains and addresses.
      - Examine the DNS cache for suspicious or anomalous entries.
        - $osquery_0
    - Examine the host services for suspicious or anomalous entries.
      - $osquery_1
      - $osquery_2
      - $osquery_3
- Investigate potentially compromised accounts. Analysts can do this by searching for login events (for example, 4624) to the host which is the source of this activity.

### False positive analysis

- Authentication misconfiguration or obsolete credentials.
- Service account password expired.
- Domain trust relationship issues.
- Infrastructure or availability issues.

### Response and remediation

- Initiate the incident response process based on the outcome of the triage.
- Isolate the source host to prevent further post-compromise behavior.
- If the asset is exposed to the internet with RDP or other remote services available, take the necessary measures to restrict access to the asset. If not possible, limit the access via the firewall to only the needed IP addresses. Also, ensure the system uses robust authentication mechanisms and is patched regularly.
- Investigate credential exposure on systems compromised or used by the attacker to ensure all compromised accounts are identified. Reset passwords for these accounts and other potentially compromised credentials, such as email, business systems, and web services.
- Run a full antimalware scan. This may reveal additional artifacts left in the system, persistence mechanisms, and malware components.
- Determine the initial vector abused by the attacker and take action to prevent reinfection through the same vector.
- Using the incident response data, update logging and audit policies to improve the mean time to detect (MTTD) and the mean time to respond (MTTR).

"""
references = ["https://docs.microsoft.com/en-us/windows/security/threat-protection/auditing/event-4625"]
risk_score = 47
rule_id = "f9790abf-bd0c-45f9-8b5f-d0b74015e029"
setup="""

If enabling an EQL rule on a non-elastic-agent index (such as beats) for versions <8.2,
events will not define `event.ingested` and default fallback for EQL rules was not added until version 8.2.
Hence for this rule to work effectively, users will need to add a custom ingest pipeline to populate
`event.ingested` to @timestamp.
For more details on adding a custom ingest pipeline refer - https://www.elastic.co/guide/en/fleet/current/data-streams-pipeline-tutorial.html
"""
severity = "medium"
tags = ["Domain: Endpoint", "OS: Windows", "Use Case: Threat Detection", "Tactic: Credential Access", "Resources: Investigation Guide"]
type = "eql"

query = '''
sequence by winlog.computer_name, source.ip with maxspan=10s
  [authentication where event.action == "logon-failed" and winlog.logon.type : "Network" and
    source.ip != null and source.ip != "127.0.0.1" and source.ip != "::1" and user.name : "*admin*" and

    /* noisy failure status codes often associated to authentication misconfiguration */
    not winlog.event_data.Status : ("0xC000015B", "0XC000005E", "0XC0000133", "0XC0000192")] with runs=5
'''


[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1110"
name = "Brute Force"
reference = "https://attack.mitre.org/techniques/T1110/"
[[rule.threat.technique.subtechnique]]
id = "T1110.001"
name = "Password Guessing"
reference = "https://attack.mitre.org/techniques/T1110/001/"

[[rule.threat.technique.subtechnique]]
id = "T1110.003"
name = "Password Spraying"
reference = "https://attack.mitre.org/techniques/T1110/003/"



[rule.threat.tactic]
id = "TA0006"
name = "Credential Access"
reference = "https://attack.mitre.org/tactics/TA0006/"