본문으로 건너뛰기
cccskills
무료GitHub에 공개

building-threat-intelligence-feed-integration

Build automated pipelines that collect threat indicators, such as malicious IP addresses and file hashes, from multiple feeds. Standardize formats, remove duplicates, assess confidence, and distribute indicators to security monitoring systems for matching and alerts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Builds automated threat intelligence feed integration pipelines connecting STIX/TAXII feeds, open-source threat intel, and commercial TI platforms into SIEM and security tools for real-time IOC matching and alerting. Use when SOC teams need to operationalize threat intelligence by automating feed ingestion, normalization, scoring, and distribution to detection systems.

설치 방법 보기

이럴 때 유용합니다

  • Automating threat feed ingestion
  • Standardizing and deduplicating threat indicators
  • Distributing indicators to multiple monitoring systems
  • Comparing feed quality through detection matches

포함된 파일(4)

  • SKILL.md11.2 KB
  • LICENSE11.0 KB
  • references/api-reference.md2.1 KB
  • scripts/agent.py6.2 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Building Threat Intelligence Feed Integration

When to Use

Use this skill when:

  • SOC teams need automated ingestion of threat intelligence feeds into SIEM platforms
  • Multiple TI sources require normalization into a common format (STIX 2.1)
  • Detection systems need real-time IOC matching against network and endpoint telemetry
  • TI feed quality assessment and deduplication processes need to be established

Do not use for manual IOC lookup — use dedicated enrichment tools (VirusTotal, AbuseIPDB) for ad-hoc queries.

Prerequisites

  • MISP instance or Threat Intelligence Platform (TIP) for feed aggregation
  • STIX/TAXII client library (taxii2-client, stix2 Python packages)
  • SIEM platform (Splunk ES, Elastic Security, or Sentinel) with TI framework configured
  • API keys for commercial and open-source feeds (AlienVault OTX, Abuse.ch, CISA AIS)
  • Python 3.8+ for feed processing automation

Workflow

Step 1: Identify and Catalog Intelligence Sources

Map available feeds by type, format, and update frequency:

Feed SourceFormatIOC TypesUpdate FreqCost
AlienVault OTXSTIX/JSONIP, Domain, Hash, URLReal-timeFree
Abuse.ch URLhausCSV/JSONURL, DomainEvery 5 minFree
Abuse.ch MalwareBazaarJSON APIFile HashReal-timeFree
CISA AISSTIX/TAXII 2.1All typesDailyFree (US Gov)
CrowdStrike IntelSTIX/JSONAll types + Actor TTPReal-timeCommercial
Mandiant AdvantageSTIX 2.1All types + ReportsReal-timeCommercial

Step 2: Ingest STIX/TAXII Feeds

Connect to a TAXII 2.1 server and download indicators:

from taxii2client.v21 import Server, Collection
from stix2 import parse

# Connect to TAXII server (example: CISA AIS)
server = Server(
    "https://taxii.cisa.gov/taxii2/",
    user="your_username",
    password="your_password"
)

# List available collections
for api_root in server.api_roots:
    print(f"API Root: {api_root.title}")
    for collection in api_root.collections:
        print(f"  Collection: {collection.title} (ID: {collection.id})")

# Fetch indicators from a collection
collection = Collection(
    "https://taxii.cisa.gov/taxii2/collections/COLLECTION_ID/",
    user="your_username",
    password="your_password"
)

# Get indicators added in last 24 hours
from datetime import datetime, timedelta
added_after = (datetime.utcnow() - timedelta(days=1)).strftime("%Y-%m-%dT%H:%M:%S.000Z")

response = collection.get_objects(added_after=added_after, type=["indicator"])
for obj in response.get("objects", []):
    indicator = parse(obj)
    print(f"Type: {indicator.type}")
    print(f"Pattern: {indicator.pattern}")
    print(f"Valid Until: {indicator.valid_until}")
    print(f"Confidence: {indicator.confidence}")
    print("---")

Step 3: Ingest Open-Source Feeds

Abuse.ch URLhaus Feed:

import requests
import csv
from io import StringIO

# Download URLhaus recent URLs
response = requests.get("https://urlhaus.abuse.ch/downloads/csv_recent/")
reader = csv.reader(StringIO(response.text), delimiter=',')

indicators = []
for row in reader:
    if row[0].startswith("#"):
        continue
    indicators.append({
        "id": row[0],
        "dateadded": row[1],
        "url": row[2],
        "url_status": row[3],
        "threat": row[5],
        "tags": row[6]
    })

print(f"Ingested {len(indicators)} URLs from URLhaus")

# Filter for active threats only
active = [i for i in indicators if i["url_status"] == "online"]
print(f"Active threats: {len(active)}")

AlienVault OTX Pulse Feed:

from OTXv2 import OTXv2, IndicatorTypes

otx = OTXv2("YOUR_OTX_API_KEY")

# Get subscribed pulses (last 24 hours)
pulses = otx.getall(modified_since="2024-03-14T00:00:00")

for pulse in pulses:
    print(f"Pulse: {pulse['name']}")
    print(f"Tags: {pulse['tags']}")
    for indicator in pulse["indicators"]:
        print(f"  IOC: {indicator['indicator']} ({indicator['type']})")

Abuse.ch Feodo Tracker (C2 IPs):

response = requests.get("https://feodotracker.abuse.ch/downloads/ipblocklist_recommended.json")
c2_data = response.json()

for entry in c2_data:
    print(f"IP: {entry['ip_address']}:{entry['port']}")
    print(f"Malware: {entry['malware']}")
    print(f"First Seen: {entry['first_seen']}")
    print(f"Last Online: {entry['last_online']}")

Step 4: Normalize and Deduplicate

Convert all feeds to STIX 2.1 format for standardization:

from stix2 import Indicator, Bundle
import hashlib

def create_stix_indicator(ioc_value, ioc_type, source, confidence=50):
    """Convert raw IOC to STIX 2.1 indicator"""
    pattern_map = {
        "ipv4": f"[ipv4-addr:value = '{ioc_value}']",
        "domain": f"[domain-name:value = '{ioc_value}']",
        "url": f"[url:value = '{ioc_value}']",
        "sha256": f"[file:hashes.'SHA-256' = '{ioc_value}']",
        "md5": f"[file:hashes.MD5 = '{ioc_value}']",
    }

    return Indicator(
        name=f"{ioc_type}: {ioc_value}",
        pattern=pattern_map[ioc_type],
        pattern_type="stix",
        valid_from="2024-03-15T00:00:00Z",
        confidence=confidence,
        labels=[source],
        custom_properties={"x_source_feed": source}
    )

# Deduplicate across sources
seen_iocs = set()
unique_indicators = []

for ioc in all_collected_iocs:
    ioc_hash = hashlib.sha256(f"{ioc['type']}:{ioc['value']}".encode()).hexdigest()
    if ioc_hash not in seen_iocs:
        seen_iocs.add(ioc_hash)
        unique_indicators.append(
            create_stix_indicator(ioc["value"], ioc["type"], ioc["source"])
        )

bundle = Bundle(objects=unique_indicators)
print(f"Unique indicators: {len(unique_indicators)}")

Step 5: Push to SIEM Threat Intelligence Framework

Push to Splunk ES Threat Intelligence:

import requests

splunk_url = "https://splunk.company.com:8089"
headers = {"Authorization": f"Bearer {splunk_token}"}

for indicator in unique_indicators:
    # Extract IOC value from STIX pattern
    ioc_value = indicator.pattern.split("'")[1]

    # Upload to Splunk ES threat intel collection
    data = {
        "ip": ioc_value,
        "description": indicator.name,
        "weight": indicator.confidence // 10,
        "threat_key": indicator.id,
        "source_feed": indicator.get("x_source_feed", "unknown")
    }

    requests.post(
        f"{splunk_url}/services/data/threat_intel/item/ip_intel",
        headers=headers, data=data,
        verify=not os.environ.get("SKIP_TLS_VERIFY", "").lower() == "true",  # Set SKIP_TLS_VERIFY=true for self-signed certs in lab environments
    )

Push to MISP for centralized management:

from pymisp import PyMISP, MISPEvent, MISPAttribute

misp = PyMISP("https://misp.company.com", "YOUR_MISP_API_KEY")

# Create event for feed batch
event = MISPEvent()
event.info = f"TI Feed Import - {datetime.now().strftime('%Y-%m-%d')}"
event.threat_level_id = 2  # Medium
event.analysis = 2  # Completed

# Add indicators as attributes
for ioc in unique_indicators:
    attr = MISPAttribute()
    attr.type = "ip-dst" if "ipv4" in ioc.pattern else "domain"
    attr.value = ioc.pattern.split("'")[1]
    attr.to_ids = True
    attr.comment = f"Source: {ioc.get('x_source_feed', 'mixed')}"
    event.add_attribute(**attr)

result = misp.add_event(event)
print(f"MISP Event created: {result['Event']['id']}")

Step 6: Monitor Feed Health and Quality

Track feed effectiveness metrics:

index=threat_intel sourcetype="threat_intel_manager"
| stats count AS total_iocs,
        dc(threat_key) AS unique_iocs,
        dc(source_feed) AS feed_count
  by source_feed
| join source_feed [
    search index=notable source="Threat Intelligence"
    | stats count AS matches by source_feed
  ]
| eval match_rate = round(matches / unique_iocs * 100, 2)
| sort - match_rate
| table source_feed, unique_iocs, matches, match_rate

Key Concepts

TermDefinition
STIX 2.1Structured Threat Information Expression — standardized JSON format for sharing threat intelligence objects
TAXIITrusted Automated eXchange of Indicator Information — transport protocol for sharing STIX data via REST API
TIPThreat Intelligence Platform — centralized system for aggregating, scoring, and distributing threat intelligence
IOC ScoringProcess of assigning confidence values to indicators based on source reliability and corroboration
Feed DeduplicationRemoving duplicate IOCs across multiple sources while preserving multi-source attribution
IOC ExpirationTime-to-live policy removing aged indicators (IP: 30 days, Domain: 90 days, Hash: 1 year)

Tools & Systems

  • MISP: Open-source threat intelligence platform for feed aggregation, correlation, and sharing
  • AlienVault OTX: Free threat intelligence sharing platform with community pulse feeds
  • Abuse.ch: Suite of free threat feeds (URLhaus, MalwareBazaar, Feodo Tracker, ThreatFox)
  • OpenCTI: Open-source cyber threat intelligence platform supporting STIX 2.1 native storage
  • TAXII2 Client: Python library for connecting to STIX/TAXII 2.1 servers for automated indicator retrieval

Common Scenarios

  • New Feed Onboarding: Evaluate feed quality, map fields to STIX, configure automated ingestion pipeline
  • Multi-SIEM Distribution: Push normalized IOCs from MISP to Splunk, Elastic, and Sentinel simultaneously
  • False Positive Reduction: Score IOCs by source count and age, expire stale indicators automatically
  • Feed Quality Audit: Compare detection match rates across feeds to identify highest-value sources
  • Incident IOC Sharing: Package investigation IOCs as STIX bundle and share with ISACs via TAXII

Output Format

THREAT INTEL FEED STATUS — Daily Report
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Date:         2024-03-15
Total IOCs:   45,892 active indicators

Feed Health:
  Feed                  IOCs    Matches  Match Rate  Status
  Abuse.ch URLhaus      12,340  47       0.38%       HEALTHY
  AlienVault OTX        18,567  23       0.12%       HEALTHY
  Abuse.ch Feodo        1,203   12       1.00%       HEALTHY
  CISA AIS              8,945   8        0.09%       HEALTHY
  CrowdStrike Intel     4,837   31       0.64%       HEALTHY

Actions Today:
  New IOCs ingested:    1,247
  IOCs expired:         892
  Duplicates removed:   156
  SIEM matches:         121 notable events generated
  False positives:      3 (CDN IPs removed from feed)

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Assess exposure of Windows secrets protected by DPAPI, the operating system’s data protection service, during authorized security tests. Guide examination and decryption of saved credentials and browser data using available user sessions, credentials, or backup keys.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing saved credential exposure in authorized tests
  • Examining collected DPAPI files offline
  • Reviewing protected browser and RDP credentials
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Assess Shadow Credentials account takeover during authorized Active Directory testing. Examine permissions that allow public key registration, validate authentication without a password reset, and document credential cleanup, detection opportunities, and access control changes needed for remediation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Assessing risky permissions identified in BloodHound
  • Validating account takeover without resetting a password
  • Generating directory change records during security exercises
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Prepare for CMMC Level 2, a cybersecurity certification for defense contractors. Define the systems handling controlled information, review security requirements, calculate the SPRS assessment score, and organize remediation plans and evidence into a readiness report.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Define the assessment scope for systems handling controlled information
  • Calculate an SPRS score and prioritize unmet security requirements
  • Prepare security plans, remediation milestones, and assessment evidence
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Create disk images for investigations using dd or dcfldd on Linux. The workflow covers write protection, bit-for-bit copying, hash checks to verify integrity, and documentation of the acquisition process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Preserving a suspect drive before investigation
  • Creating evidence copies of USB devices or memory cards
  • Verifying that a disk image matches its source
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Review Active Directory permissions with ldap3, a Python library for directory queries. Identify excessive access granted to non-administrative accounts, explain potential privilege escalation paths, and produce a JSON report with affected objects and remediation recommendations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Auditing excessive Active Directory permissions
  • Investigating privilege escalation paths from ordinary accounts
  • Reporting affected objects and permission remediation recommendations
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

Inspect suspicious Android APK files without running them. Analyze permissions, app components, suspicious code patterns, and network indicators, then produce a JSON risk report to support malware triage and detection rule development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Triage suspicious APK files without running them
  • Inspect dangerous permission combinations and suspicious API calls
  • Extract network indicators for malware detection rules
mukul975/Anthropic-Cybersecurity-Skills3.5만2026년 8월 31일 업데이트

mukul975의 스킬 모두 보기

이 스킬의 문제 신고