Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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MISP (Malware Information Sharing Platform) is an open-source threat intelligence platform for gathering, sharing, storing, and correlating Indicators of Compromise (IOCs) of targeted attacks, threat
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MISP (Malware Information Sharing Platform) is an open-source threat intelligence platform for gathering, sharing, storing, and correlating Indicators of Compromise (IOCs) of targeted attacks, threat intelligence, financial fraud information, vulnerability information, or counter-terrorism information. This skill covers deploying MISP, configuring threat feeds, using the PyMISP API for programmatic access, and building automated collection pipelines that aggregate IOCs from multiple community and commercial sources.
pymisp library installedMISP operates on an event-based model where threat intelligence is organized into events containing attributes (IOCs), objects (structured groupings of attributes), galaxies (threat actor/malware clusters linked to MITRE ATT&CK), and tags for classification. Synchronization between MISP instances uses a pull/push model over HTTPS with API key authentication.
PyMISP is the official Python library to access MISP platforms via their REST API. It supports fetching events, adding/updating events and attributes, uploading samples, and searching across the entire MISP dataset. Authentication uses an API key passed in the Authorization header.
git clone https://github.com/MISP/misp-docker.git
cd misp-docker
cp template.env .env
# Edit .env to set MISP_BASEURL, MISP_ADMIN_EMAIL, MISP_ADMIN_PASSPHRASE
docker compose up -d
Enable built-in MISP feeds via the web UI or API:
from pymisp import PyMISP
misp = PyMISP('https://misp.local', 'YOUR_API_KEY', ssl=False)
# List available feeds
feeds = misp.feeds()
for feed in feeds:
print(f"{feed['Feed']['id']}: {feed['Feed']['name']} - Enabled: {feed['Feed']['enabled']}")
# Enable CIRCL OSINT Feed
misp.enable_feed(feed_id=1)
misp.cache_feed(feed_id=1)
misp.fetch_feed(feed_id=1)
# Add abuse.ch URLhaus feed
feed_data = {
'name': 'URLhaus Recent URLs',
'provider': 'abuse.ch',
'url': 'https://urlhaus.abuse.ch/downloads/csv_recent/',
'source_format': 'csv',
'input_source': 'network',
'publish': False,
'enabled': True,
'headers': '',
'distribution': 0,
'sharing_group_id': 0,
'tag_id': 0,
'default': False,
'lookup_visible': True
}
result = misp.add_feed(feed_data)
print(f"Feed added: {result}")
from pymisp import PyMISP, MISPEvent
from datetime import datetime, timedelta
misp = PyMISP('https://misp.local', 'YOUR_API_KEY', ssl=False)
# Search for events from the last 7 days
result = misp.search(
controller='events',
date_from=(datetime.now() - timedelta(days=7)).strftime('%Y-%m-%d'),
type_attribute='ip-dst',
to_ids=True,
pythonify=True
)
for event in result:
print(f"Event {event.id}: {event.info}")
for attr in event.attributes:
if attr.type == 'ip-dst' and attr.to_ids:
print(f" IOC: {attr.value} (category: {attr.category})")
# Export as STIX 2.1 bundle
stix_output = misp.search(
controller='events',
return_format='stix2',
tags=['tlp:white'],
published=True
)
# Export IDS-flagged attributes as Suricata rules
suricata_rules = misp.search(
controller='attributes',
return_format='suricata',
to_ids=True,
type_attribute=['ip-dst', 'domain', 'url']
)
# Export as CSV for SIEM ingestion
csv_output = misp.search(
controller='attributes',
return_format='csv',
type_attribute='ip-dst',
to_ids=True
)
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Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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Detect dangerous ACL misconfigurations in Active Directory using ldap3 to identify GenericAll, WriteDACL, and WriteOwner abuse paths
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Perform static analysis of Android APK malware samples using apktool for decompilation, jadx for Java source recovery, and androguard for permission analysis, manifest inspection, and suspicious API call detection.
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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.
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Analyze advanced persistent threat (APT) group techniques using MITRE ATT&CK Navigator to create layered heatmaps of adversary TTPs for detection gap analysis and threat-informed defense.
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Queries Azure Monitor activity logs and sign-in logs via azure-monitor-query to detect suspicious administrative operations, impossible travel, privilege escalation, and resource modifications. Builds KQL queries for threat hunting in Azure environments. Use when investigating suspicious Azure tenant activity or building cloud SIEM detections.
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