Threat Detection

# Threat Detection

Threat detection skill for proactive discovery of attacker activity through hypothesis-driven hunting, IOC analysis, and behavioral anomaly detection. This is NOT incident response (see incident-response) or red team operations (see red-team) — this is about finding threats that have evaded automated controls.

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## Table of Contents

- [Overview](#overview)
- [Threat Signal Analyzer](#threat-signal-analyzer)
- [Threat Hunting Methodology](#threat-hunting-methodology)
- [IOC Analysis](#ioc-analysis)
- [Anomaly Detection](#anomaly-detection)
- [MITRE ATT&CK Signal Prioritization](#mitre-attck-signal-prioritization)
- [Deception and Honeypot Integration](#deception-and-honeypot-integration)
- [Workflows](#workflows)
- [Anti-Patterns](#anti-patterns)
- [Cross-References](#cross-references)

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## Overview

### What This Skill Does

This skill provides the methodology and tooling for **proactive threat detection** — finding attacker activity through structured hunting hypotheses, IOC analysis, and statistical anomaly detection before alerts fire.

### Distinction from Other Security Skills

| Skill | Focus | Approach |
|-------|-------|----------|
| **threat-detection** (this) | Finding hidden threats | Proactive — hunt before alerts |
| incident-response | Active incidents | Reactive — contain and investigate declared incidents |
| red-team | Offensive simulation | Offensive — test defenses from attacker perspective |
| cloud-security | Cloud misconfigurations | Posture — IAM, S3, network exposure |

### Prerequisites

Read access to SIEM/EDR telemetry, endpoint logs, and network flow data. IOC feeds require freshness within 30 days to avoid false positives. Hunting hypotheses must be scoped to the environment before execution.

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## Threat Signal Analyzer

The `threat_signal_analyzer.py` tool supports three modes: `hunt` (hypothesis scoring), `ioc` (sweep generation), and `anomaly` (statistical detection).

```bash
# Hunt mode: score a hypothesis against MITRE ATT&CK coverage
python3 scripts/threat_signal_analyzer.py --mode hunt \
  --hypothesis "Lateral movement via PtH using compromised service account" \
  --actor-relevance 3 --control-gap 2 --data-availability 2 --json

# IOC mode: generate sweep targets from an IOC feed file
python3 scripts/threat_signal_analyzer.py --mode ioc \
  --ioc-file iocs.json --json

# Anomaly mode: detect statistical outliers in telemetry events
python3 scripts/threat_signal_analyzer.py --mode anomaly \
  --events-file telemetry.json \
  --baseline-mean 100 --baseline-std 25 --json

# List all supported MITRE ATT&CK techniques
python3 scripts/threat_signal_analyzer.py --list-techniques
```

### IOC file format

```json
{
  "ips": ["1.2.3.4", "5.6.7.8"],
  "domains": ["malicious.example.com"],
  "hashes": ["abc123def456..."]
}
```

### Telemetry events file format

```json
[
  {"timestamp": "2024-01-15T14:32:00Z", "entity": "host-01", "action": "dns_query", "volume": 450},
  {"timestamp": "2024-01-15T14:33:00Z", "entity": "host-02", "action": "dns_query", "volume": 95}
]
```

### Exit codes

| Code | Meaning |
|------|---------|
| 0 | No high-priority findings |
| 1 | Medium-priority signals detected |
| 2 | High-priority confirmed findings |

---

## Threat Hunting Methodology

Structured threat hunting follows a five-step loop: hypothesis → data source identification → query execution → finding triage → feedback to detection engineering.

### Hypothesis Scoring

| Factor | Weight | Description |
|--------|--------|-------------|
| Actor relevance | ×3 | How closely does this TTP match known threat actors in your sector? |
| Control gap | ×2 | How many of your existing controls