<!-- Copyright (c) 2026 defconxt. All rights reserved. -->
<!-- Licensed under AGPL-3.0 — see LICENSE file for details. -->
---
name: building-behavioral-detection-models
description: >-
  Design behavioral detection models for anomaly detection including
  baseline profiling, statistical analysis, machine learning integration,
  user behavior analytics, and entity risk scoring.
domain: cybersecurity
subdomain: detection-engineering
tags:
  - behavioral-detection
  - anomaly-detection
  - baseline-profiling
  - uba
  - entity-risk-scoring
  - statistical-analysis
version: "1.0"
author: defconxt
license: AGPL-3.0
metadata:
  mitre-attack: ["T1078"]
---

# Building Behavioral Detection Models

## Overview

Behavioral detection identifies anomalous activity through baselining
and deviation analysis. Covers user and entity profiling, statistical
thresholds, ML integration, risk scoring, and alert prioritization.

## Prerequisites

| Requirement | Purpose |
|---|---|
| Baseline data | 30-90 days of normal activity |
| Analytics platform | SIEM with stats or UBA tool |
| Data science tools | Python pandas, scikit-learn |

## Key Concepts

### Behavioral Detection Architecture

```python
baseline_model = {
    "entity_type": "user",
    "features": {
        "login_hours": {"mean": 9.0, "std": 1.5, "threshold": 3.0},
        "daily_auth_count": {"mean": 12, "std": 4, "threshold": 2.5},
        "unique_systems": {"mean": 5, "std": 2, "threshold": 2.0},
        "data_volume_mb": {"mean": 150, "std": 50, "threshold": 3.0},
    },
    "risk_weights": {
        "login_anomaly": 0.3, "access_anomaly": 0.25,
        "volume_anomaly": 0.25, "pattern_anomaly": 0.2,
    },
}
```

## Workflow

1. **Define behaviors** — Identify normal patterns to baseline
2. **Collect baseline** — 30-90 days of activity data per entity
3. **Build profiles** — Statistical profiling with mean, std, percentiles
4. **Set thresholds** — Deviation thresholds for anomaly alerting
5. **Implement scoring** — Weighted risk scoring across dimensions
6. **Validate detections** — Test against known-good and known-bad
7. **Tune and iterate** — Adjust based on FP rates and efficacy

## Detection

```yaml
title: Behavioral Detection Models Detection
id: 7002f20f-2b7a-449d-b041-5aea2e3a2dc1
status: experimental
description: Detects suspicious activity related to building behavioral detection models techniques in detection engineering context
logsource:
  category: process_creation
  product: windows
detection:
  selection:
    CommandLine: "*building*behavioral*"
  condition: selection
level: medium
tags:
  - attack.t1078
  - attack.defense_evasion
falsepositives:
  - Detection rule testing in staging SIEM environment
```

## Verification

| Check | Method |
|---|---|
| Workflow completed | All steps executed and documented |
| Output validated | Results match expected format and content |
| Quality assured | Peer review or automated validation passed |

## References

- [NIST Cybersecurity Framework](https://www.nist.gov/cyberframework)
- [MITRE ATT&CK](https://attack.mitre.org/)
