Analyzing Threat Landscape With Misp

---
name: analyzing-threat-landscape-with-misp
description: Use when analyzing the threat landscape using MISP (Malware Information
  Sharing Platform) by querying event statistics, attribute distributions, threat
  actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data,
  compute IOC type breakdowns, identify top threat actors and malware families, and
  generate threat landscape reports with temporal trends.
domain: cybersecurity
tags:
- analyzing
- threat
- landscape
- with
subdomain: threat-intelligence
version: '1.0'
author: oyi77
license: Apache-2.0
d3fend_techniques:
- File Metadata Consistency Validation
- Application Protocol Command Analysis
- Identifier Analysis
- Content Format Conversion
- Message Analysis
nist_csf:
- ID.RA-01
- ID.RA-05
- DE.CM-01
- DE.AE-02
category: cybersecurity
---

# Analyzing Threat Landscape With Misp

## Overview

Cybersecurity skill for analyzing threat landscape with misp. Follows industry best practices and security standards.

## When to Use
**Trigger phrases:**
- "analyzing threat landscape with misp"
- "Analyze the threat landscape using MISP (Malware Information Sharing Platform) b"


- When investigating security incidents that require analyzing threat landscape with misp
- When building detection rules or threat hunting queries for this domain
- When SOC analysts need structured procedures for this analysis type
- When validating security monitoring coverage for related attack techniques


## When NOT to Use

- When you lack proper authorization for testing
- For production systems without change management
- When the task requires legal or compliance expertise beyond technical scope


## Prerequisites

- Familiarity with threat intelligence concepts and tools
- Access to a test or lab environment for safe execution
- Python 3.8+ with required dependencies installed
- Appropriate authorization for any testing activities

## Workflow

```python
# Example: IOC detection
import re

IOC_PATTERNS = {
    "ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
    "domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
    "hash_md5": r"\b[a-f0-9]{32}\b",
    "hash_sha256": r"\b[a-f0-9]{64}\b",
}

def extract_iocs(text: str) -> dict:
    return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
```

1. **Scope the Analysis** — Define what threat landscape artifacts or data sources to examine and the investigation timeline.
2. **Preserve Evidence** — Create forensic copies of relevant data. Maintain chain of custody documentation.
3. **Extract Key Indicators** — Use misp to parse and extract relevant threat landscape data points from collected artifacts.
4. **Correlate Findings** — Cross-reference extracted data with other sources (threat intel, logs, timelines).
5. **Build Timeline** — Construct a chronological sequence of events related to threat landscape.
6. **Document Analysis** — Write findings report with evidence, conclusions, and recommendations.

## Tools

- **misp** — Primary tool for this skill
- **Forensic Toolkit** — Evidence collection and analysis
- **Timeline Tools** — Chronological event reconstruction
- **Log Analysis Platform** — Centralized log parsing and search


## Process

1. **Scope** — Define research questions, identify data sources, set time boundaries
1. **Gather** — Collect data from primary sources, APIs, and public records
1. **Synthesize** — Analyze findings, identify patterns, produce actionable report

## Verification

- [ ] All threat landscape procedures executed completely and documented
- [ ] Findings validated against multiple data sources
- [ ] False positives identified and filtered
- [ ] Results documented with evidence and timestamps
- [ ] Recommendations provided with risk-based prioritization

## Anti-Rationalization Table

| Rationalization | Reality |
|---|---|
| "We are too small to be targeted" | Automated attacks target everyone. Size does not matter. |
| "Security slows us down" | A breach slows you down 100x more. Build security in from the start. |
| "We will fix it after launch" | Vulnerabilities in production are exploited within hours. Fix before deploy. |