performing-ioc-enrichment-automation
Automates Indicator of Compromise (IOC) enrichment by orchestrating lookups across VirusTotal, AbuseIPDB, Shodan, MISP, and other intelligence sources to provide contextual scoring and disposition recommendations. Use when SOC analysts need rapid multi-source enrichment of IPs, domains, URLs, and file hashes during alert triage or incident investigation.
- License 许可
- Apache-2.0
- 最近更新
- 2026-08-31
- Stars
- ★ 3.6w
- Forks
- 4.4k
01 前言 Front Matter
- name
- performing-ioc-enrichment-automation
- tags
- [ "soc", "ioc", "enrichment", "automation", "virustotal", "abuseipdb", "shodan", "threat-intelligence" ]
- author
- mahipal
- domain
- cybersecurity
- license
- Apache-2.0
- version
- 1.0
- nist_csf
- [ "DE.CM-01", "DE.AE-02", "RS.MA-01", "DE.AE-06" ]
- subdomain
- soc-operations
- description
- Automates Indicator of Compromise (IOC) enrichment by orchestrating lookups across VirusTotal, AbuseIPDB, Shodan, MISP, and other intelligence sources to provide contextual scoring and disposition recommendations. Use when SOC analysts need rapid multi-source enrichment of IPs, domains, URLs, and file hashes during alert triage or incident investigation.
- mitre_attack
- [ "T1078", "T1685.002", "T1685.005", "T1566" ]
02 资源文件 3
- 许可证LICENSE11.0 KB
- 参考文档references/api-reference.md1.7 KB
- 脚本scripts/agent.py9.0 KB
03 SKILL.md 正文
Performing IOC Enrichment Automation
When to Use
Use this skill when:
- SOC analysts need to quickly enrich IOCs from multiple sources during alert triage
- High alert volumes require automated enrichment to reduce manual lookup time
- Incident investigations need comprehensive IOC context for scope assessment
- SOAR playbooks require enrichment actions as part of automated triage workflows
Do not use for bulk blocking decisions without analyst review — enrichment provides context, not definitive malicious/benign determination.
Prerequisites
- API keys: VirusTotal (free or premium), AbuseIPDB, Shodan, URLScan.io, GreyNoise
- Python 3.8+ with
requests,vt-py,shodanlibraries - MISP instance or TIP for cross-referencing organizational intelligence
- SOAR platform (optional) for workflow integration
- Rate limit awareness: VT free (4 req/min), AbuseIPDB (1000/day), Shodan (1 req/sec)
Workflow
Step 1: Build Unified Enrichment Engine
Create a multi-source enrichment pipeline:
import requests
import vt
import shodan
import time
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class EnrichmentResult:
ioc_value: str
ioc_type: str
virustotal: dict = field(default_factory=dict)
abuseipdb: dict = field(default_factory=dict)
shodan_data: dict = field(default_factory=dict)
greynoise: dict = field(default_factory=dict)
urlscan: dict = field(default_factory=dict)
misp_matches: list = field(default_factory=list)
risk_score: float = 0.0
disposition: str = "Unknown"
class IOCEnrichmentEngine:
def __init__(self, config):
self.vt_client = vt.Client(config["virustotal_key"])
self.shodan_api = shodan.Shodan(config["shodan_key"])
self.abuseipdb_key = config["abuseipdb_key"]
self.greynoise_key = config["greynoise_key"]
self.urlscan_key = config["urlscan_key"]
def enrich_ip(self, ip_address):
result = EnrichmentResult(ioc_value=ip_address, ioc_type="ip")
# VirusTotal
try:
vt_obj = self.vt_client.get_object(f"/ip_addresses/{ip_address}")
result.virustotal = {
"malicious": vt_obj.last_analysis_stats.get("malicious", 0),
"suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
"total_engines": sum(vt_obj.last_analysis_stats.values()),
"reputation": vt_obj.reputation,
"country": getattr(vt_obj, "country", "Unknown"),
"as_owner": getattr(vt_obj, "as_owner", "Unknown")
}
except Exception as e:
result.virustotal = {"error": str(e)}
# AbuseIPDB
try:
response = requests.get(
"https://api.abuseipdb.com/api/v2/check",
headers={"Key": self.abuseipdb_key, "Accept": "application/json"},
params={"ipAddress": ip_address, "maxAgeInDays": 90}
)
data = response.json()["data"]
result.abuseipdb = {
"confidence_score": data["abuseConfidenceScore"],
"total_reports": data["totalReports"],
"is_tor": data.get("isTor", False),
"usage_type": data.get("usageType", "Unknown"),
"isp": data.get("isp", "Unknown"),
"domain": data.get("domain", "Unknown")
}
except Exception as e:
result.abuseipdb = {"error": str(e)}
# Shodan
try:
host = self.shodan_api.host(ip_address)
result.shodan_data = {
"ports": host.get("ports", []),
"os": host.get("os", "Unknown"),
"organization": host.get("org", "Unknown"),
"isp": host.get("isp", "Unknown"),
"vulns": host.get("vulns", []),
"last_update": host.get("last_update", "Unknown")
}
except shodan.APIError:
result.shodan_data = {"status": "Not found in Shodan"}
# GreyNoise
try:
response = requests.get(
f"https://api.greynoise.io/v3/community/{ip_address}",
headers={"key": self.greynoise_key}
)
gn_data = response.json()
result.greynoise = {
"classification": gn_data.get("classification", "unknown"),
"noise": gn_data.get("noise", False),
"riot": gn_data.get("riot", False),
"name": gn_data.get("name", "Unknown")
}
except Exception as e:
result.greynoise = {"error": str(e)}
# Calculate composite risk score
result.risk_score = self._calculate_ip_risk(result)
result.disposition = self._determine_disposition(result.risk_score)
return result
def enrich_domain(self, domain):
result = EnrichmentResult(ioc_value=domain, ioc_type="domain")
# VirusTotal
try:
vt_obj = self.vt_client.get_object(f"/domains/{domain}")
result.virustotal = {
"malicious": vt_obj.last_analysis_stats.get("malicious", 0),
"suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
"reputation": vt_obj.reputation,
"creation_date": getattr(vt_obj, "creation_date", "Unknown"),
"registrar": getattr(vt_obj, "registrar", "Unknown"),
"categories": getattr(vt_obj, "categories", {})
}
except Exception as e:
result.virustotal = {"error": str(e)}
# URLScan.io
try:
response = requests.get(
f"https://urlscan.io/api/v1/search/?q=domain:{domain}",
headers={"API-Key": self.urlscan_key}
)
scans = response.json().get("results", [])
result.urlscan = {
"total_scans": len(scans),
"verdicts": [s.get("verdicts", {}).get("overall", {}).get("malicious", False)
for s in scans[:5]],
"last_scan": scans[0]["task"]["time"] if scans else "Never scanned"
}
except Exception as e:
result.urlscan = {"error": str(e)}
result.risk_score = self._calculate_domain_risk(result)
result.disposition = self._determine_disposition(result.risk_score)
return result
def enrich_hash(self, file_hash):
result = EnrichmentResult(ioc_value=file_hash, ioc_type="hash")
# VirusTotal
try:
vt_obj = self.vt_client.get_object(f"/files/{file_hash}")
result.virustotal = {
"malicious": vt_obj.last_analysis_stats.get("malicious", 0),
"suspicious": vt_obj.last_analysis_stats.get("suspicious", 0),
"undetected": vt_obj.last_analysis_stats.get("undetected", 0),
"total_engines": sum(vt_obj.last_analysis_stats.values()),
"type_description": getattr(vt_obj, "type_description", "Unknown"),
"popular_threat_name": getattr(vt_obj, "popular_threat_classification", {}).get(
"suggested_threat_label", "Unknown"
),
"sandbox_verdicts": getattr(vt_obj, "sandbox_verdicts", {}),
"first_seen": getattr(vt_obj, "first_submission_date", "Unknown")
}
except vt.APIError:
result.virustotal = {"status": "Not found in VirusTotal"}
# MalwareBazaar
try:
response = requests.post(
"https://mb-api.abuse.ch/api/v1/",
data={"query": "get_info", "hash": file_hash}
)
mb_data = response.json()
if mb_data["query_status"] == "ok":
entry = mb_data["data"][0]
result.abuseipdb = { # Reusing field for MalwareBazaar data
"malware_family": entry.get("signature", "Unknown"),
"tags": entry.get("tags", []),
"file_type": entry.get("file_type", "Unknown"),
"delivery_method": entry.get("delivery_method", "Unknown"),
"first_seen": entry.get("first_seen", "Unknown")
}
except Exception:
pass
result.risk_score = self._calculate_hash_risk(result)
result.disposition = self._determine_disposition(result.risk_score)
return result
def _calculate_ip_risk(self, result):
score = 0
vt = result.virustotal
abuse = result.abuseipdb
gn = result.greynoise
if isinstance(vt, dict) and "malicious" in vt:
score += min(vt["malicious"] * 3, 30)
if isinstance(abuse, dict) and "confidence_score" in abuse:
score += abuse["confidence_score"] * 0.3
if isinstance(gn, dict):
if gn.get("classification") == "malicious":
score += 20
elif gn.get("riot"):
score -= 20 # Known benign service
return min(max(score, 0), 100)
def _calculate_domain_risk(self, result):
score = 0
vt = result.virustotal
if isinstance(vt, dict) and "malicious" in vt:
score += min(vt["malicious"] * 4, 40)
if vt.get("reputation", 0) < -5:
score += 20
return min(max(score, 0), 100)
def _calculate_hash_risk(self, result):
score = 0
vt = result.virustotal
if isinstance(vt, dict) and "malicious" in vt:
total = vt.get("total_engines", 1)
detection_rate = vt["malicious"] / total if total > 0 else 0
score = detection_rate * 100
return min(max(score, 0), 100)
def _determine_disposition(self, risk_score):
if risk_score >= 70:
return "MALICIOUS — Block recommended"
elif risk_score >= 40:
return "SUSPICIOUS — Monitor and investigate"
elif risk_score >= 10:
return "LOW RISK — Likely benign, verify context"
else:
return "CLEAN — No indicators of malicious activity"
def close(self):
self.vt_client.close()
Step 2: Batch Enrichment for Incident Investigation
# Process multiple IOCs from an incident
iocs = [
{"type": "ip", "value": "185.234.218.50"},
{"type": "domain", "value": "evil-c2-server.com"},
{"type": "hash", "value": "a1b2c3d4e5f6..."},
{"type": "ip", "value": "45.33.32.156"},
]
config = {
"virustotal_key": "YOUR_VT_KEY",
"shodan_key": "YOUR_SHODAN_KEY",
"abuseipdb_key": "YOUR_ABUSEIPDB_KEY",
"greynoise_key": "YOUR_GREYNOISE_KEY",
"urlscan_key": "YOUR_URLSCAN_KEY"
}
engine = IOCEnrichmentEngine(config)
results = []
for ioc in iocs:
if ioc["type"] == "ip":
result = engine.enrich_ip(ioc["value"])
elif ioc["type"] == "domain":
result = engine.enrich_domain(ioc["value"])
elif ioc["type"] == "hash":
result = engine.enrich_hash(ioc["value"])
results.append(result)
time.sleep(15) # Rate limiting for free VT API
engine.close()
# Print summary
for r in results:
print(f"{r.ioc_type}: {r.ioc_value}")
print(f" Risk Score: {r.risk_score}")
print(f" Disposition: {r.disposition}")
print()
Step 3: Integrate with Splunk for Automated Enrichment
Create a Splunk custom search command for inline enrichment:
index=notable sourcetype="stash"
| table src_ip, dest_ip, file_hash, url
| lookup threat_intel_ip_lookup ip AS src_ip OUTPUT vt_score, abuse_score, disposition
| lookup threat_intel_hash_lookup hash AS file_hash OUTPUT vt_detections, malware_family
| eval combined_risk = coalesce(vt_score, 0) + coalesce(abuse_score, 0)
| where combined_risk > 50
| sort - combined_risk
Step 4: Generate Enrichment Report
def generate_enrichment_report(results):
report = []
report.append("IOC ENRICHMENT REPORT")
report.append("=" * 60)
for r in sorted(results, key=lambda x: x.risk_score, reverse=True):
report.append(f"\n{r.ioc_type.upper()}: {r.ioc_value}")
report.append(f" Risk Score: {r.risk_score}/100")
report.append(f" Disposition: {r.disposition}")
if r.virustotal and "malicious" in r.virustotal:
report.append(f" VirusTotal: {r.virustotal['malicious']}/{r.virustotal.get('total_engines', 'N/A')} malicious")
if r.abuseipdb and "confidence_score" in r.abuseipdb:
report.append(f" AbuseIPDB: {r.abuseipdb['confidence_score']}% confidence, {r.abuseipdb['total_reports']} reports")
if r.greynoise and "classification" in r.greynoise:
report.append(f" GreyNoise: {r.greynoise['classification']}")
if r.shodan_data and "ports" in r.shodan_data:
report.append(f" Shodan: Ports {r.shodan_data['ports']}, Org: {r.shodan_data.get('organization', 'N/A')}")
return "\n".join(report)
Key Concepts
| Term | Definition |
|---|---|
| IOC Enrichment | Process of adding contextual intelligence to raw indicators from multiple external sources |
| Composite Risk Score | Weighted aggregate score combining multiple intelligence sources for disposition decisions |
| Rate Limiting | API request restrictions requiring throttling (VT free: 4/min, AbuseIPDB: 1000/day) |
| GreyNoise RIOT | Rule It Out — GreyNoise dataset of known benign services to reduce false positives |
| Passive DNS | Historical DNS resolution data showing domain-to-IP mappings over time |
| Defanging | Modifying IOCs for safe handling in reports (evil.com becomes evil[.]com) |
Tools & Systems
- VirusTotal: Multi-engine malware scanner providing file, URL, IP, and domain analysis with 70+ AV engines
- AbuseIPDB: Community IP reputation database with abuse confidence scoring and ISP attribution
- Shodan: Internet-wide scanner providing open ports, banners, and vulnerability data for IP addresses
- GreyNoise: Internet noise intelligence distinguishing targeted attacks from opportunistic scanning
- URLScan.io: URL analysis platform capturing screenshots, DOM, and network requests for phishing detection
Common Scenarios
- Alert Triage Enrichment: Auto-enrich all IPs in a notable event to determine if source is known malicious
- Incident Scope Assessment: Batch-enrich all IOCs from a compromised host to identify C2 infrastructure
- Threat Intel Validation: Enrich received IOC feed to validate quality before adding to blocking controls
- Phishing URL Analysis: Enrich URLs from reported phishing emails with URLScan and VT before user notification
- False Positive Investigation: Enrich flagged IP to determine if it belongs to CDN/cloud provider (legitimate)
Output Format
IOC ENRICHMENT REPORT — IR-2024-0450
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Enrichment Time: 2024-03-15 14:30 UTC
IOCs Processed: 4
IP: 185.234.218[.]50
Risk Score: 87/100 — MALICIOUS
VirusTotal: 14/90 engines flagged malicious
AbuseIPDB: 92% confidence, 347 reports
Shodan: Ports [22, 80, 443, 4444], Org: BulletProof Hosting
GreyNoise: malicious — known C2 infrastructure
Action: BLOCK immediately
DOMAIN: evil-c2-server[.]com
Risk Score: 73/100 — MALICIOUS
VirusTotal: 8/90 engines flagged
URLScan: 5 scans, 4 malicious verdicts
WHOIS: Registered 3 days ago via Namecheap
Action: BLOCK and add to DNS sinkhole
HASH: a1b2c3d4e5f6...
Risk Score: 91/100 — MALICIOUS
VirusTotal: 52/72 engines (Cobalt Strike Beacon)
MalwareBazaar: Tags: cobalt-strike, beacon, c2
Action: BLOCK hash, quarantine affected endpoints
IP: 45.33.32[.]156
Risk Score: 5/100 — CLEAN
VirusTotal: 0/90 engines
GreyNoise: benign — Shodan scanner
Action: No action required (known scanner)
04 同仓库更多 Skills
verifying-build-provenance-with-slsa-sigstore
Verifies artifact signatures and SLSA provenance using Sigstore's cosign (verify, verify-attestation, verify-blob-attestation) and slsa-verifier (verify-artifact), enforcing keyless OIDC builder identity and source repo against SLSA Build levels. Use in CI/CD before deploying artifacts, when consuming third-party attestations, establishing a SLSA Build L3 pipeline, or confirming provenance during incident response or admission control.
validating-tpm-measured-boot-attestation
Verifies TPM 2.0 measured-boot integrity and remote attestation with tpm2-tools -- reading PCRs (tpm2_pcrread), replaying the boot event log, generating and checking signed quotes (tpm2_quote/tpm2_checkquote), and sealing secrets to a PCR policy. Use to confirm a system booted trusted firmware/kernel for Zero Trust device posture, detect boot-chain tampering via PCR drift, or build a golden-value baseline for fleet attestation.
validating-backup-integrity-for-recovery
Validates backup integrity through cryptographic hash verification, automated restore testing, corruption detection, and recoverability checks to confirm backups are reliable for disaster recovery and ransomware response. Use before relying on backups for recovery, when building post-backup validation pipelines, auditing backup infrastructure for compliance, or checking immutable/air-gapped backups for silent corruption or tampering.
triaging-windows-with-kape
Runs KAPE (Kroll Artifact Parser and Extractor) to collect targeted forensic artifacts (registry hives, $MFT, event logs, prefetch, browser data) via Targets and parse them with Modules wrapping Eric Zimmerman's EZ Tools (PECmd, MFTECmd, RECmd). Use during early incident containment/triage when full disk imaging is impractical but a defensible, parseable Windows artifact set is needed quickly, including at-scale remote collection.
triaging-vulnerabilities-with-ssvc-framework
Triages and prioritizes vulnerabilities with CISA's Stakeholder-Specific Vulnerability Categorization (SSVC) decision tree, weighing exploitation status (via the CISA KEV catalog and FIRST EPSS API), technical impact, automatability, and mission prevalence to output Track/Track*/Attend/Act decisions. Use when prioritizing vulnerability scan results (OpenVAS, Nessus, Qualys) for remediation planning beyond raw CVSS scores.
triaging-security-incident
Performs initial triage of security incidents using the NIST SP 800-61r3 and SANS PICERL frameworks, classifying incident type, assigning priority by business impact, and routing to the appropriate response team. Use when a SIEM/EDR alert needs human classification, concurrent alerts must be prioritized, or a user report or threat-intel IOC match requires initial incident categorization.