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---
technique: building-security-event-correlation-engine
subdomain: security-data-engineering
title: "Building Security Event Correlation Engine"
description: "Techniques and methodologies for building security event correlation engine"
tags:
  - event-correlation
  - SIEM
  - complex-event-processing
  - alert-triage
  - data-fusion
metadata:
  mitre-attack: ["T1070"]
---

# Building Security Event Correlation Engine

> Design and implement engines that correlate security events across multiple data sources to identify complex attack patterns and reduce alert fatigue.

## Overview

Security event correlation engines aggregate and analyze events from disparate sources—firewalls, EDR, IAM, cloud logs—to surface multi-stage attacks that individual alerts miss. This technique covers architecture patterns, correlation rule design, and performance optimization for high-volume environments.

## Core Concepts

- **Event normalization**: Transform heterogeneous log formats into a unified schema (ECS, OCSF)
- **Temporal correlation**: Link events within sliding time windows to detect attack sequences
- **Graph-based correlation**: Model entity relationships (IP, user, host) to identify lateral movement
- **Statistical baselining**: Establish normal behavior profiles for anomaly-based correlation
- **Rule chaining**: Compose atomic detections into composite alerts with kill-chain context

## Workflow

1. Define correlation objectives aligned with MITRE ATT&CK tactics
2. Design the event normalization pipeline and unified schema
3. Implement temporal and graph-based correlation logic
4. Build statistical baselines for anomaly detection
5. Create correlation rule library with severity scoring
6. Deploy engine with horizontal scaling for throughput
7. Validate against historical attack datasets
8. Tune false positive rates and alert prioritization
9. Integrate with SOAR for automated response actions
10. Monitor engine health metrics and correlation latency

## Implementation

```python
from dataclasses import dataclass, field
from datetime import datetime
from typing import Optional
import hashlib
import json


@dataclass
class NormalizedEvent:
    """Unified event representation for correlation."""
    event_id: str
    timestamp: datetime
    source: str
    event_type: str
    severity: int
    entities: dict = field(default_factory=dict)
    raw: dict = field(default_factory=dict)

    def fingerprint(self) -> str:
        key = f"{self.source}:{self.event_type}:{json.dumps(self.entities, sort_keys=True)}"
        return hashlib.sha256(key.encode()).hexdigest()[:16]


class CorrelationEngine:
    """Multi-source security event correlation."""

    def __init__(self, window_seconds: int = 300) -> None:
        self.window_seconds = window_seconds
        self.rules: list[dict] = []
        self.event_buffer: list[NormalizedEvent] = []

    def add_rule(self, name: str, conditions: list[dict], severity: int) -> None:
        self.rules.append({"name": name, "conditions": conditions, "severity": severity})

    def ingest(self, event: NormalizedEvent) -> list[dict]:
        self.event_buffer.append(event)
        self._prune_buffer()
        return self._evaluate_rules(event)

    def _prune_buffer(self) -> None:
        if not self.event_buffer:
            return
        cutoff = self.event_buffer[-1].timestamp.timestamp() - self.window_seconds
        self.event_buffer = [
            e for e in self.event_buffer if e.timestamp.timestamp() >= cutoff
        ]

    def _evaluate_rules(self, trigger: NormalizedEvent) -> list[dict]:
        alerts = []
        for rule in self.rules:
            if self._rule_matches(rule, trigger):
                alerts.append({"rule": rule["name"], "severity": rule["severity"], "trigger": trigger.event_id})
        return alerts

    def _rule_matches(self, rule: dict, trigger: NormalizedEvent) -> bool:
        for cond in rule["conditions"]:
            if cond.get("event_type") == trigger.event_type:
                return True
        return False
```

## Best Practices

- Normalize events to a common schema before correlation
- Use sliding windows rather than fixed intervals for temporal correlation
- Implement circuit breakers for correlation rule evaluation under load
- Version correlation rules and track detection efficacy metrics
- Test correlation logic against known attack playbooks (Atomic Red Team)
- Monitor correlation engine latency—alerting delays cost response time

## Detection

```yaml
title: Security Event Correlation Engine Detection
id: cc1b92b7-70bb-471b-ab09-d2346333f098
status: experimental
description: Detects suspicious activity related to building security event correlation engine techniques in security data engineering context
logsource:
  category: process_creation
  product: linux
detection:
  selection:
    CommandLine: "*building*security*"
  condition: selection
level: medium
tags:
  - attack.t1070
  - attack.collection
falsepositives:
  - Data pipeline ETL processes during scheduled batch windows
```


**Detection Opportunities**

| Indicator | Source | Detection Logic |
|---|---|---|
| Security Event Correlation Engine Detection | linux/process_creation | Sigma rule (medium) |
| ATT&CK Coverage | MITRE ATT&CK | T1070 |

## Related Techniques

- [Performing Detection Signal Analysis](../performing-detection-signal-analysis/SKILL.md)
- [Implementing Security Data Classification](../implementing-security-data-classification/SKILL.md)
