Files
sigma-rules/rules/linux/impact_potential_bruteforce_malware_infection.toml
T
Terrance DeJesus b28338c680 [Rule Tuning] ESQL Query Field Dynamic Field Standardization (#4912)
* adjusted Potential Widespread Malware Infection Across Multiple Hosts

* adjusted Microsoft Azure or Mail Sign-in from a Suspicious Source

* adjusted AWS EC2 Multi-Region DescribeInstances API Calls

* adjusted AWS Discovery API Calls via CLI from a Single Resource

* adjusted AWS Service Quotas Multi-Region  Requests

* adjusted AWS EC2 EBS Snapshot Shared or Made Public

* adjusted AWS S3 Bucket Enumeration or Brute Force

* adjusted AWS EC2 EBS Snapshot Access Removed

* adjusted Potential AWS S3 Bucket Ransomware Note Uploaded

* adjusted AWS S3 Object Encryption Using External KMS Key

* adjusted AWS S3 Static Site JavaScript File Uploaded

* adjusted AWS Access Token Used from Multiple Addresses

* adjusted AWS Signin Single Factor Console Login with Federated User

* adjusted AWS IAM AdministratorAccess Policy Attached to Group

* adjusted AWS IAM AdministratorAccess Policy Attached to Role

* adjusted AWS IAM AdministratorAccess Policy Attached to User

* adjusted AWS Bedrock Invocations without Guardrails Detected by a Single User Over a Session

* adjusted AWS Bedrock Guardrails Detected Multiple Violations by a Single User Over a Session

* adjusted AWS Bedrock Guardrails Detected Multiple Policy Violations Within a Single Blocked Request

* adjusted Unusual High Confidence Content Filter Blocks Detected

* adjusted Potential Abuse of Resources by High Token Count and Large Response Sizes

* AWS Bedrock Detected Multiple Attempts to use Denied Models by a Single User

* Unusual High Denied Sensitive Information Policy Blocks Detected

* adjusted Unusual High Denied Topic Blocks Detected

* adjusted AWS Bedrock Detected Multiple Validation Exception Errors by a Single User

* adjusted Unusual High Word Policy Blocks Detected

* adjusted Microsoft Entra ID Concurrent Sign-Ins with Suspicious Properties

* adjusted Azure Entra MFA TOTP Brute Force Attempts

* adjusted Microsoft Entra ID Sign-In Brute Force Activity

* adjusted Microsoft Entra ID Exccessive Account Lockouts Detected

* adjusted Microsoft 365 Brute Force via Entra ID Sign-Ins

* deprecated Azure Entra Sign-in Brute Force Microsoft 365 Accounts by Repeat Source

* adjusted Microsoft Entra ID Session Reuse with Suspicious Graph Access

* adjusted Suspicious Microsoft OAuth Flow via Auth Broker to DRS

* adjusted Potential Denial of Azure OpenAI ML Service

* adjusted Azure OpenAI Insecure Output Handling

* adjusted Potential Azure OpenAI Model Theft

* adjusted M365 OneDrive Excessive File Downloads with OAuth Token

* adjusted Multiple Microsoft 365 User Account Lockouts in Short Time Window

* adjusted Potential Microsoft 365 User Account Brute Force

* adjusted Suspicious Microsoft 365 UserLoggedIn via OAuth Code

* adjusted Multiple Device Token Hashes for Single Okta Session

* adjusted Multiple Okta User Authentication Events with Client Address

* adjusted Multiple Okta User Authentication Events with Same Device Token Hash

* adjusted High Number of Okta Device Token Cookies Generated for Authentication

* adjusted Okta User Sessions Started from Different Geolocations

* adjusted High Number of Egress Network Connections from Unusual Executable

* adjusted Unusual Base64 Encoding/Decoding Activity

* adjusted Potential Port Scanning Activity from Compromised Host

* adjusted Potential Subnet Scanning Activity from Compromised Host

* adjusted Unusual File Transfer Utility Launched

* adjusted Potential Malware-Driven SSH Brute Force Attempt

* adjusted Unusual Process Spawned from Web Server Parent

* adjusted Unusual Command Execution from Web Server Parent

* adjusted  Rare Connection to WebDAV Target

* adjusted Potential PowerShell Obfuscation via Invalid Escape Sequences

* adjusted Potential PowerShell Obfuscation via Backtick-Escaped Variable Expansion

* adjusted Unusual File Creation by Web Server

* adjusted Potential PowerShell Obfuscation via High Special Character Proportion

* adjusted Potential Malicious PowerShell Based on Alert Correlation

* adjusted Potential PowerShell Obfuscation via Character Array Reconstruction

* adjusted Potential PowerShell Obfuscation via String Reordering

* adjusted Potential PowerShell Obfuscation via String Concatenation

* adjusted Potential PowerShell Obfuscation via Reverse Keywords

* adjusted PowerShell Obfuscation via Negative Index String Reversal

* adjusted Dynamic IEX Reconstruction via Method String Access

* adjusted Potential Dynamic IEX Reconstruction via Environment Variables

* adjusted Potential PowerShell Obfuscation via High Numeric Character Proportion

* adjusted Potential PowerShell Obfuscation via Concatenated Dynamic Command Invocation

* adjusted Rare Connection to WebDAV Target

* adjusted Potential PowerShell Obfuscation via Invalid Escape Sequences

* adjusted Potential PowerShell Obfuscation via Backtick-Escaped Variable Expansion

* adjusted Potential PowerShell Obfuscation via Character Array Reconstruction

* adjusted Potential PowerShell Obfuscation via High Special Character Proportion

* adjusted Potential PowerShell Obfuscation via Special Character Overuse

* adjusted Potential PowerShell Obfuscation via String Reordering

* adjusted Suspicious Microsoft 365 UserLoggedIn via OAuth Code

* adjusted fields that were inconsistent

* adjusted additional fields

* adjusted esql to Esql

* adjusted several rules for common field names

* updating rules

* updated dates

* updated dates

* updated ESQL fields

* lowercase all functions and logical operators

* adjusted dates for unit tests

* Update Esql_priv to Esql_temp as these don't hold PII

* PowerShell adjustments

* Make query comments consistent

* update comment

* reverted 2856446a-34e6-435b-9fb5-f8f040bfa7ed

* Update rules/windows/discovery_command_system_account.toml

* removed dot notation

---------

Co-authored-by: Jonhnathan <26856693+w0rk3r@users.noreply.github.com>
2025-08-05 19:35:41 -04:00

173 lines
9.7 KiB
TOML

[metadata]
creation_date = "2025/02/20"
integration = ["endpoint"]
maturity = "production"
updated_date = "2025/07/16"
[rule]
author = ["Elastic"]
description = """
This detection identifies a Linux host that has potentially been infected with malware and is being used to conduct
brute-force attacks against external systems over SSH (port 22 and common alternative SSH ports). The detection looks
for a high volume of outbound connection attempts to non-private IP addresses from a single process. A compromised host
may be part of a botnet or controlled by an attacker, attempting to gain unauthorized access to remote systems. This
behavior is commonly observed in SSH brute-force campaigns where malware hijacks vulnerable machines to expand its
attack surface. ESQL rules have limited fields available in its alert documents. Make sure to review the original
documents to aid in the investigation of this alert.
"""
from = "now-61m"
interval = "1h"
language = "esql"
license = "Elastic License v2"
name = "Potential Malware-Driven SSH Brute Force Attempt"
note = """ ## Triage and analysis
> **Disclaimer**:
> This investigation guide was created using generative AI technology and has been reviewed to improve its accuracy and relevance. While every effort has been made to ensure its quality, we recommend validating the content and adapting it to suit your specific environment and operational needs.
### Investigating Potential Malware-Driven SSH Brute Force Attempt
SSH is a protocol used to securely access remote systems. Adversaries exploit it by deploying malware on compromised Linux hosts to perform brute-force attacks, attempting unauthorized access to other systems. The detection rule identifies such abuse by monitoring high volumes of outbound SSH connection attempts from a single process to external IPs, indicating potential malware activity.
### Possible investigation steps
- Review the process executable identified in the alert to determine if it is a legitimate application or potentially malicious. Check for known malware signatures or unusual file paths.
- Analyze the destination IP addresses involved in the connection attempts to identify if they are known malicious hosts or part of a larger attack infrastructure. Use threat intelligence sources to gather more information.
- Examine the host's recent activity logs to identify any unusual behavior or signs of compromise, such as unexpected process executions or changes in system configurations.
- Investigate the specific agent.id associated with the alert to determine if other alerts or suspicious activities have been reported from the same host, indicating a broader compromise.
- Check for any recent changes or updates to the host's software or configurations that could have introduced vulnerabilities exploited by the malware.
- Assess the network traffic patterns from the host to identify any other unusual outbound connections that may indicate additional malicious activity or data exfiltration attempts.
### False positive analysis
- High-volume legitimate SSH operations from a single process can trigger alerts. Exclude known safe processes or scripts that perform frequent SSH operations by adding them to an exception list.
- Automated backup or synchronization tools using SSH to connect to external servers may be misidentified. Identify these tools and exclude their process names or IP addresses from the detection rule.
- Development or testing environments where SSH connections are frequently initiated to external systems for legitimate purposes can cause false positives. Document these environments and adjust the rule to exclude their specific IP ranges or process identifiers.
- Security scanning tools that perform SSH checks on external systems might be flagged. Ensure these tools are recognized and their activities are excluded by specifying their process names or IP addresses in the rule exceptions.
### Response and remediation
- Isolate the affected Linux host from the network immediately to prevent further unauthorized access attempts and potential spread of malware to other systems.
- Terminate the suspicious process identified by the detection rule to stop ongoing brute-force attempts and reduce the risk of further compromise.
- Conduct a thorough malware scan on the isolated host using updated antivirus or endpoint detection and response (EDR) tools to identify and remove any malicious software.
- Review and reset credentials for any accounts that may have been targeted or compromised during the brute-force attempts to ensure account security.
- Apply security patches and updates to the affected host and any other vulnerable systems to mitigate known vulnerabilities that could be exploited by similar threats.
- Monitor network traffic for any signs of continued or new suspicious activity, particularly focusing on outbound SSH connections, to detect and respond to any further attempts promptly.
- Escalate the incident to the security operations center (SOC) or relevant security team for further investigation and to assess the potential impact on the broader network infrastructure.
"""
risk_score = 47
rule_id = "77122db4-5876-4127-b91b-6c179eb21f88"
setup = """## Setup
This rule requires data coming in from Elastic Defend.
### Elastic Defend Integration Setup
Elastic Defend is integrated into the Elastic Agent using Fleet. Upon configuration, the integration allows the Elastic Agent to monitor events on your host and send data to the Elastic Security app.
#### Prerequisite Requirements:
- Fleet is required for Elastic Defend.
- To configure Fleet Server refer to the [documentation](https://www.elastic.co/guide/en/fleet/current/fleet-server.html).
#### The following steps should be executed in order to add the Elastic Defend integration on a Linux System:
- Go to the Kibana home page and click "Add integrations".
- In the query bar, search for "Elastic Defend" and select the integration to see more details about it.
- Click "Add Elastic Defend".
- Configure the integration name and optionally add a description.
- Select the type of environment you want to protect, either "Traditional Endpoints" or "Cloud Workloads".
- Select a configuration preset. Each preset comes with different default settings for Elastic Agent, you can further customize these later by configuring the Elastic Defend integration policy. [Helper guide](https://www.elastic.co/guide/en/security/current/configure-endpoint-integration-policy.html).
- We suggest selecting "Complete EDR (Endpoint Detection and Response)" as a configuration setting, that provides "All events; all preventions"
- Enter a name for the agent policy in "New agent policy name". If other agent policies already exist, you can click the "Existing hosts" tab and select an existing policy instead.
For more details on Elastic Agent configuration settings, refer to the [helper guide](https://www.elastic.co/guide/en/fleet/8.10/agent-policy.html).
- Click "Save and Continue".
- To complete the integration, select "Add Elastic Agent to your hosts" and continue to the next section to install the Elastic Agent on your hosts.
For more details on Elastic Defend refer to the [helper guide](https://www.elastic.co/guide/en/security/current/install-endpoint.html).
"""
severity = "medium"
tags = [
"Domain: Endpoint",
"OS: Linux",
"Use Case: Threat Detection",
"Tactic: Impact",
"Tactic: Execution",
"Tactic: Command and Control",
"Data Source: Elastic Defend",
"Resources: Investigation Guide",
]
timestamp_override = "event.ingested"
type = "esql"
query = '''
from logs-endpoint.events.network-*
| keep @timestamp, host.os.type, event.type, event.action, destination.port, process.executable, destination.ip, agent.id, host.name
| where
@timestamp > now() - 1 hours and
host.os.type == "linux" and
event.type == "start" and
event.action == "connection_attempted" and
destination.port in (22, 222, 2222, 10022, 2022, 2200, 62612, 8022) and
not cidr_match(
destination.ip,
"10.0.0.0/8", "127.0.0.0/8", "169.254.0.0/16", "172.16.0.0/12",
"192.0.0.0/24", "192.0.0.0/29", "192.0.0.8/32", "192.0.0.9/32",
"192.0.0.10/32", "192.0.0.170/32", "192.0.0.171/32", "192.0.2.0/24",
"192.31.196.0/24", "192.52.193.0/24", "192.168.0.0/16", "192.88.99.0/24",
"224.0.0.0/4", "100.64.0.0/10", "192.175.48.0/24", "198.18.0.0/15",
"198.51.100.0/24", "203.0.113.0/24", "240.0.0.0/4", "::1", "FE80::/10", "FF00::/8"
)
| stats
Esql.event_count = count(),
Esql.agent_id_count_distinct = count_distinct(agent.id),
Esql.host_name_values = values(host.name),
Esql.agent_id_values = values(agent.id)
by process.executable, destination.port
| where
Esql.agent_id_count_distinct == 1 and
Esql.event_count > 15
| sort Esql.event_count asc
| limit 100
'''
[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1496"
name = "Resource Hijacking"
reference = "https://attack.mitre.org/techniques/T1496/"
[rule.threat.tactic]
id = "TA0040"
name = "Impact"
reference = "https://attack.mitre.org/tactics/TA0040/"
[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1059"
name = "Command and Scripting Interpreter"
reference = "https://attack.mitre.org/techniques/T1059/"
[[rule.threat.technique.subtechnique]]
id = "T1059.004"
name = "Unix Shell"
reference = "https://attack.mitre.org/techniques/T1059/004/"
[rule.threat.tactic]
id = "TA0002"
name = "Execution"
reference = "https://attack.mitre.org/tactics/TA0002/"
[[rule.threat]]
framework = "MITRE ATT&CK"
[[rule.threat.technique]]
id = "T1071"
name = "Application Layer Protocol"
reference = "https://attack.mitre.org/techniques/T1071/"
[rule.threat.tactic]
id = "TA0011"
name = "Command and Control"
reference = "https://attack.mitre.org/tactics/TA0011/"