MITRE CAR

MITRE CAR: Bridging Adversarial Tactics and Advanced Detection Analytics

The MITRE Cyber Analytics Repository (CAR) is an advanced, structured, and open repository of analytics that provide security teams with the means to detect, monitor, and mitigate adversary behaviors described in the MITRE ATT&CK Framework. By defining specific detection methodologies for ATT&CK techniques, CAR bridges the gap between theoretical adversarial tactics and their practical detection in real-world environments.

What is MITRE CAR?

MITRE CAR defines detection methodologies and maps them to specific ATT&CK techniques, enabling security teams to:

• Detect specific TTPs (tactics, techniques, and procedures) employed by adversaries.

• Identify required telemetry sources for implementing analytics.

• Tailor detection strategies to their unique environment (e.g., Windows, Linux, macOS, or hybrid cloud systems).

Unlike ATT&CK, which catalogs adversary behaviors, CAR focuses on operationalizing detections by providing:

• Analytics Specifications: Detailed descriptions of how to detect specific adversary actions.

• Telemetry Requirements: Information about the data sources and events needed to implement the analytics.

• Mapping to ATT&CK: Direct associations between CAR analytics and ATT&CK techniques and tactics.
  

Structure of a CAR Analytic

Each CAR analytic is a modular, structured document designed to provide actionable insights for security professionals. It typically includes the following sections:

1. Analytic Description

Purpose: High-level overview of the analytic and its objectives.

Example: "This analytic detects attempts to access LSASS memory, commonly associated with credential dumping tools like Mimikatz."

2. ATT&CK Mapping

• Tactics: The higher-level adversary goal (e.g., Credential Access, Defense Evasion).

• Techniques: The specific ATT&CK technique being detected (e.g., T1003.001 - LSASS Memory Dumping).

• Sub-Techniques: If applicable, the analytic may target sub-techniques (e.g., Memory Access - T1003.001).
  

3. Detection Logic

Definition: The analytic’s detection logic, typically presented as pseudocode or human-readable logic.

ExampleLogic for LSASS Access Detection:

IF process_name == "ProcDump.exe" OR "Mimikatz.exe"

AND target_process == "LSASS.exe"

AND access_type == "memory_read"

THEN generate_alert("Potential LSASS memory access detected")

4. Required Telemetry

Defines the necessary telemetry sources and data fields for the analytic to function.

Example Telemetry Sources:

• Process creation logs (e.g., Sysmon Event ID 1).

• Memory access monitoring (e.g., Windows Security Audit).

• Command-line arguments (e.g., Sysmon Event ID 10).
  

Data Model in CAR

MITRE CAR uses a data model to define and normalize telemetry requirements across environments. This ensures analytics are interoperable and can be adapted to various platforms and tools.

Key Data Model Components

Entities:

• Process: Information about processes, including names, parent processes, and execution arguments.

• File: Metadata about files, such as hash values, sizes, and locations.

• Network Flow: Data on network traffic, including source/destination IPs and ports.

• Registry Key: Information about registry changes (Windows-specific).

Fields:

Each entity has specific fields that must be collected and analyzed.

Process Entity Example:

• process_name: Name of the process (e.g., Mimikatz.exe).

• command_line: Command-line arguments passed during execution

• parent_process_name: Name of the parent process.

Telemetry Sources:

CAR analytics often reference specific telemetry types:

• Endpoint Monitoring: EDR solutions like CrowdStrike or Carbon Black.

• Network Monitoring: Flow data from tools like Zeek (formerly Bro).

• Log Aggregation: SIEM platforms like Splunk or Elastic Stack.
  

Operationalizing CAR

Implementing CAR analytics in real-world environments requires several steps:

1. Mapping ATT&CK Techniques to CAR Analytics

CAR provides pre-built analytics for ATT&CK techniques. Security teams can map their existing detection infrastructure to these analytics.

Example Mapping:

ATT&CK Technique: T1059.001 - PowerShell Execution.

CAR Analytic: "Detecting Malicious PowerShell Commands."

Logic: Analyze command_line fields for suspicious patterns such as encoded commands or outbound network connections.

Telemetry: Sysmon Event ID 1 (process creation), Windows Event ID 4688 (process tracking).

2. Customizing Analytics

CAR analytics are modular and can be adapted to specific environments or platforms:

Translate pseudocode into detection rules for specific tools (e.g., YARA, Splunk queries, or Elastic DSL).

Adjust analytics based on unique environmental baselines (e.g., known benign PowerShell scripts in DevOps environments).

3. Deploying Detection Rules

Use SIEM, EDR, or SOAR platforms to implement detection logic.

Example: Splunk Query for Process Monitoring

index=endpoint sourcetype=sysmon process_name="PowerShell.exe"command_line="*base64*"

This query detects base64-encoded commands in PowerShell execution.

4. Validating Analytics

Test analytics using adversary emulation tools such as:

• Atomic Red Team: Simulate ATT&CK techniques for validation.

• CALDERA: Automate adversary behavior to evaluate detection efficacy.

Example CAR Analytic: Detecting LSASS Memory Access

Analytic Overview

Name: Detecting LSASS Memory Access.

Purpose: Identify unauthorized memory access to the LSASS process, a hallmark of credential dumping.

ATT&CK Technique: T1003.001 - LSASS Memory Dumping.

Detection Logic

• Monitor for processes interacting with LSASS.exe.

• Identify known credential dumping tools (Mimikatz, ProcDump, etc.).

• Correlate telemetry for anomalous memory access patterns.

IF process_name IN ["ProcDump.exe", "Mimikatz.exe"]AND target_process_name == "LSASS.exe"AND memory_access == "read"THEN alert("Potential credential dumping detected")

Telemetry Requirements

Data Sources:

• Process monitoring (Sysmon Event ID 10).

• Memory access monitoring (Windows Security Logs).
  

Fields:

• process_name

• target_process_name

• access_type
  

Challenges in CAR Implementation

1. Telemetry Gaps

Not all organizations collect the necessary telemetry, such as memory access events or detailed process creation logs.

2. Custom Data Pipelines

CAR’s analytics require standardized data pipelines. Organizations may need to preprocess logs for compatibility with CAR’s data model.

3. False Positives

• Certain analytics may trigger false positives without tuning. For example:

• Detecting PowerShell scripts may flag legitimate administrative activity unless baselines are established.
  

Advantages of Using CAR

• Actionable Detection: CAR bridges the gap between theoretical ATT&CK techniques and real-world detection.

• Standardized Framework: The data model ensures consistency across platforms and tools.

• Community-Driven: Open contributions enable rapid adaptation to emerging threats.
  

Conclusion

The MITRE Cyber Analytics Repository (CAR) is an indispensable resource for translating adversary tactics into practical, platform-specific detection methodologies. By aligning CAR analytics with the ATT&CK Framework, organizations can build a proactive and responsive detection strategy tailored to their environment.

For technical teams, CAR provides the tools, logic, and telemetry mappings needed to operationalize advanced threat detection and close the gap between knowing adversarial behaviors and mitigating them effectively.