building-threat-feed-aggregation-with-misp skill
Deploy MISP via Docker and configure feeds from sources like abuse.ch, AlienVault OTX, and CIRCL to aggregate, correlate, and distribute threat intelligence, including automated feed synchronization and STIX/TAXII-based integration with Splunk, Elasticsearch, and SOAR platforms. Use when standing up centralized IOC management or wiring multi-source threat feeds into a SIEM.
Is the building-threat-feed-aggregation-with-misp skill safe?
Clean: nothing in its files matched our rules. We read 4 files in the folder on 2026-09-28.
No findings.
Install the building-threat-feed-aggregation-with-misp skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git /tmp/Anthropic-Cybersecurity-Skills mkdir -p ~/.claude/skills cp -r /tmp/Anthropic-Cybersecurity-Skills/skills/building-threat-feed-aggregation-with-misp ~/.claude/skills/building-threat-feed-aggregation-with-misp
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
Building Threat Feed Aggregation with MISP
Overview
MISP is the leading open-source threat intelligence platform for collecting, storing, distributing, and sharing cybersecurity indicators and threat intelligence. It aggregates feeds from OSINT sources, commercial providers, and sharing communities into a unified platform with automatic correlation, STIX/TAXII export, and direct integration with SIEMs and security tools. This skill covers deploying MISP via Docker, configuring feeds from sources like abuse.ch, AlienVault OTX, and CIRCL, setting up automated feed synchronization, and integrating with Splunk, Elasticsearch, and SOAR platforms.
When to Use
- When deploying or configuring building threat feed aggregation with misp capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Docker and Docker Compose for deployment
- Python 3.9+ with pymisp library for API interaction
- Linux server with 8GB+ RAM for production deployment
- Understanding of IOC types and threat intelligence lifecycle
- Network access to external feed URLs
Key Concepts
MISP Architecture
MISP stores threat intelligence as Events containing Attributes (IOCs) organized by type and category. Events can have Tags (MITRE ATT&CK, TLP marking, sector tags), Galaxies (threat actor profiles, malware families, attack patterns), and Objects (structured groupings of related attributes). Events are correlated automatically across the instance.
Feed Types
MISP supports three feed formats: MISP format (native JSON events), CSV (comma-separated IOCs), and freetext (unstructured text with automatic IOC extraction). Feeds can be remote (fetched from URLs) or local (uploaded files). MISP ships with 80+ default OSINT feeds including abuse.ch URLhaus, Botvrij, CIRCL OSINT, and malware traffic analysis.
Sharing and Synchronization
MISP instances can synchronize with other MISP instances via push/pull mechanisms. Sharing groups control distribution (organization only, this community, connected communities, all communities). The TAXII server module enables integration with STIX/TAXII consumers.
Workflow
Step 1: Deploy MISP with Docker
# docker-compose.yml for MISP deployment
version: '3.8'
services:
misp:
image: coolacid/misp-docker:core-latest
container_name: misp
restart: unless-stopped
ports:
- "443:443"
- "80:80"
environment:
- MYSQL_HOST=misp-db
- MYSQL_DATABASE=misp
- MYSQL_USER=misp
- MYSQL_PASSWORD=misp_db_password_change_me
- MISP_ADMIN_EMAIL=admin@organization.com
- MISP_ADMIN_PASSPHRASE=admin_password_change_me
- MISP_BASEURL=https://misp.organization.com
- POSTFIX_RELAY_HOST=smtp.organization.com
- TIMEZONE=UTC
volumes:
- misp-data:/var/www/MISP/app/files
- misp-config:/var/www/MISP/app/Config
depends_on:
- misp-db
- misp-redis
misp-db:
image: mysql:8.0
container_name: misp-db
restart: unless-stopped
environment:
- MYSQL_DATABASE=misp
- MYSQL_USER=misp
- MYSQL_PASSWORD=misp_db_password_change_me
- MYSQL_ROOT_PASSWORD=root_password_change_me
volumes:
- misp-db-data:/var/lib/mysql
misp-redis:
image: redis:7
container_name: misp-redis
restart: unless-stopped
volumes:
misp-data:
misp-config:
misp-db-data:Step 2: Configure Feeds via PyMISP API
from pymisp import PyMISP, MISPFeed
import json
class MISPFeedManager:
def __init__(self, misp_url, misp_key, verify_ssl=False):
self.misp = PyMISP(misp_url, misp_key, verify_ssl)
print(f"[+] Connected to MISP: {misp_url}")
def list_feeds(self):
"""List all configured feeds."""
feeds = self.misp.feeds()
enabled = [f for f in feeds if f.get("Feed", {}).get("enabled")]
disabled = [f for f in feeds if not f.get("Feed", {}).get("enabled")]
print(f"[+] Feeds: {len(enabled)} enabled, {len(disabled)} disabled")
return feeds
def enable_default_feeds(self):
"""Enable recommended default OSINT feeds."""
recommended_feeds = [
"CIRCL OSINT Feed",
"Botvrij.eu - Indicators of Compromise",
"abuse.ch URLhaus Host file",
"abuse.ch Feodo Tracker",
"abuse.ch SSL Blacklist",
"malwaredomainlist",
"CyberCure - IP Feed",
]
feeds = self.misp.feeds()
enabled_count = 0
for feed in feeds:
feed_data = feed.get("Feed", {})
if feed_data.get("name") in recommended_feeds:
Step 3: Search and Correlate Indicators
def search_indicators(misp, value=None, type_attribute=None, tags=None, last_days=30):
"""Search MISP for indicators with correlation."""
from datetime import datetime, timedelta
date_from = (datetime.now() - timedelta(days=last_days)).strftime("%Y-%m-%d")
search_params = {
"date_from": date_from,
"published": True,
"enforceWarninglist": True,
}
if value:
search_params["value"] = value
if type_attribute:
search_params["type_attribute"] = type_attribute
if tags:
search_params["tags"] = tags
results = misp.search("attributes", **search_params)
attributes = results.get("Attribute", [])
print(f"[+] Search returned {len(attributes)} attributes")
# Group by event for context
events = {}
for attr in attributes:
event_id = attr.get("event_id", "")
if event_id not in events:
events[event_id] = {"attributes": [], "tags": set()}
events[event_id]["attributes"].append({
"type": attr.get("type", ""),
"value": attr.get("value", ""),
"category": attr.get("category", ""),
"timestamp": attr.get("timestamp", ""),Step 4: Export to SIEM (Splunk / Elasticsearch)
import requests
from datetime import datetime, timedelta
class MISPSIEMExporter:
def __init__(self, misp_client):
self.misp = misp_client
def export_to_splunk(self, splunk_url, hec_token, days=7):
"""Export recent MISP indicators to Splunk via HEC."""
date_from = (datetime.now() - timedelta(days=days)).strftime("%Y-%m-%d")
results = self.misp.search("attributes", date_from=date_from,
published=True, enforceWarninglist=True)
attributes = results.get("Attribute", [])
headers = {"Authorization": f"Splunk {hec_token}"}
exported = 0
for attr in attributes:
event = {
"event": {
"ioc_type": attr.get("type", ""),
"ioc_value": attr.get("value", ""),
"category": attr.get("category", ""),
"event_id": attr.get("event_id", ""),
"timestamp": attr.get("timestamp", ""),
"tags": [t.get("name", "") for t in attr.get("Tag", [])],
},
"sourcetype": "misp:attribute",
"source": "misp",
"Validation Criteria
- MISP deployed and accessible via web interface and API
- Default OSINT feeds enabled and fetching data
- Custom feeds added and ingesting indicators
- Indicators searchable with correlation across events
- IOCs exported to SIEM (Splunk/Elasticsearch) successfully
- Blocklists generated for firewall/proxy integration
References
- MISP Project
- MISP GitHub Repository
- MISP Default Feeds
- PyMISP Documentation
- Kraven Security: MISP Using Feeds
- Cosive: What is MISP
More skills from mukul975/Anthropic-Cybersecurity-Skills
- Aabusing-dpapi-for-credential-accessExtract and decrypt Windows DPAPI-protected secrets (Credential Manager, browser logins/cookies, Wi-Fi credentials, KeePass keys) online or offline using SharpDPAPI, SharpChrome, Mimikatz, or Impacket's dpapi.py, including domain-wide decryption via the DPAPI backup key. Use during authorized red-team credential-access engagements after gaining a foothold or when triaging DPAPI blobs pulled from a host.
- Aabusing-shadow-credentials-for-privescTake over Active Directory accounts by writing attacker-controlled public keys to msDS-KeyCredentialLink (Shadow Credentials) with pyWhisker, Whisker, or Certipy, then authenticate via PKINIT to recover the target's NT hash without a password reset. Use when BloodHound shows GenericWrite/GenericAll/AddKeyCredentialLink over a target, as a stealthier alternative to ForceChangePassword, during authorized red-team engagements.
- Aachieving-cmmc-level-2-compliancePrepare a defense-contractor environment for CMMC Level 2 certification: scope CUI and FCI, implement the 110 NIST SP 800-171 Rev 2 security requirements across 14 families, compute the SPRS score with the DoD Assessment Methodology, manage a compliant POA&M, and ready the organization for a C3PAO assessment. Use when an organization handles Controlled Unclassified Information (CUI) under a DoD contract, when a contract carries DFARS clause 252.204-7012/7019/7020/7021, when preparing for or responding to a CMMC assessment, when computing or improving an SPRS score, when building a System Security Plan or POA&M for 800-171, or when scoping which systems are in the CUI boundary. Keywords: CMMC, CMMC Level 2, NIST 800-171, SP 800-171 Rev 2, CUI, FCI, SPRS, DFARS 7012, C3PAO, POA&M, System Security Plan, DoD Assessment Methodology, 110 controls, defense industrial base, DIB, FedRAMP equivalency.
- Aacquiring-disk-image-with-dd-and-dcflddCreate forensically sound bit-for-bit disk images with dd or dcfldd on a Linux forensic workstation, preserving evidence integrity through hash verification (MD5/SHA) during acquisition. Use when imaging a suspect drive, USB device, or memory card for investigation, preserving volatile disk evidence during incident response, or producing a verified copy for legal or law-enforcement proceedings before any destructive analysis.
- Aanalyzing-active-directory-acl-abuseDetect dangerous ACL misconfigurations in Active Directory using ldap3
- Aanalyzing-android-malware-with-apktoolPerform static analysis of Android APK malware using apktool for resource decompilation, jadx for Java source recovery, and androguard for manifest inspection, dangerous permission-combination detection, and identification of obfuscated code, dynamic code loading, and reflection-based API calls. Use to statically triage a suspicious APK without executing it or to build mobile malware detection rules.
- Danalyzing-api-gateway-access-logs'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect
- Aanalyzing-apt-group-with-mitre-navigatorQuery ATT&CK data with attackcti, mitreattack-python, and stix2, then build MITRE ATT&CK Navigator layers and multi-layer heatmap overlays mapping one or more APT groups' TTPs for detection-gap analysis. Use to compare threat-actor technique coverage, find gaps in detection engineering, or produce Navigator visualizations for threat-intel reporting.
- Aanalyzing-azure-activity-logs-for-threats'Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query
- Aanalyzing-bootkit-and-rootkit-samples'Analyzes bootkit and advanced rootkit malware infecting the Master
- Aanalyzing-browser-forensics-with-hindsightParse Chromium-based browser databases with Hindsight to extract and correlate browsing history, downloads, cookies, cached content, autofill data, saved passwords, and extensions from Chrome, Edge, Brave, Opera, and Vivaldi into a unified timeline (XLSX, JSON, or SQLite output). Use during incident response, insider-threat investigations, or criminal cases when you need to reconstruct a user's web activity from a browser profile.
- Aanalyzing-campaign-attribution-evidenceSystematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use when an incident investigation needs a defensible attribution confidence level.