Create forensically sound bit-for-bit disk images using dd and dcfldd while preserving evidence integrity through hash verification.
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OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using O
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
OpenCTI is an open-source platform for managing cyber threat intelligence knowledge, built on STIX 2.1 as its native data model. This skill covers building an automated IOC enrichment pipeline using OpenCTI's connector ecosystem to enrich indicators with context from VirusTotal, Shodan, AbuseIPDB, GreyNoise, and other sources. The pipeline automatically enriches newly ingested indicators, correlates them with known threat actors and campaigns, and scores them for analyst prioritization.
pycti libraryOpenCTI uses a GraphQL API frontend backed by ElasticSearch for storage and Redis/RabbitMQ for connector communication. Data is natively stored as STIX 2.1 objects with relationships. Connectors are categorized as: External Import (feed ingestion), Internal Import (file parsing), Internal Enrichment (context addition), and Stream (real-time export).
Internal enrichment connectors are triggered automatically when new observables are created or manually by analysts. Each connector receives STIX objects, queries external services, and returns STIX 2.1 bundles that augment the original observable with additional context, labels, and relationships.
OpenCTI uses a 0-100 confidence scale for indicators. Enrichment connectors can update confidence scores based on external validation: VirusTotal detection ratios, Shodan exposure data, AbuseIPDB report counts, and GreyNoise classification results.
# docker-compose.yml (key services)
version: '3'
services:
opencti:
image: opencti/platform:6.4.4
environment:
- APP__PORT=8080
- APP__ADMIN__EMAIL=admin@opencti.io
- APP__ADMIN__PASSWORD=ChangeMeNow
- APP__ADMIN__TOKEN=your-admin-token-uuid
- ELASTICSEARCH__URL=http://elasticsearch:9200
- MINIO__ENDPOINT=minio
- RABBITMQ__HOSTNAME=rabbitmq
ports:
- "8080:8080"
depends_on:
- elasticsearch
- minio
- rabbitmq
- redis
connector-virustotal:
image: opencti/connector-virustotal:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-virustotal-id
- CONNECTOR_NAME=VirusTotal
- CONNECTOR_SCOPE=StixFile,Artifact,IPv4-Addr,Domain-Name,Url
- CONNECTOR_AUTO=true
- VIRUSTOTAL_TOKEN=your-vt-api-key
- VIRUSTOTAL_MAX_TLP=TLP:AMBER
connector-shodan:
image: opencti/connector-shodan:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-shodan-id
- CONNECTOR_NAME=Shodan
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- SHODAN_TOKEN=your-shodan-api-key
- SHODAN_MAX_TLP=TLP:AMBER
connector-abuseipdb:
image: opencti/connector-abuseipdb:6.4.4
environment:
- OPENCTI_URL=http://opencti:8080
- OPENCTI_TOKEN=your-admin-token-uuid
- CONNECTOR_ID=connector-abuseipdb-id
- CONNECTOR_NAME=AbuseIPDB
- CONNECTOR_SCOPE=IPv4-Addr
- CONNECTOR_AUTO=true
- ABUSEIPDB_API_KEY=your-abuseipdb-key
import os
from pycti import OpenCTIConnectorHelper, get_config_variable
from stix2 import (
Bundle, Indicator, Note, Relationship,
IPv4Address, DomainName
)
import requests
class CustomEnrichmentConnector:
def __init__(self):
config = {
"opencti": {
"url": os.environ.get("OPENCTI_URL"),
"token": os.environ.get("OPENCTI_TOKEN"),
},
"connector": {
"id": os.environ.get("CONNECTOR_ID"),
"name": "CustomEnrichment",
"scope": "IPv4-Addr,Domain-Name,Url",
"auto": True,
"type": "INTERNAL_ENRICHMENT",
},
}
self.helper = OpenCTIConnectorHelper(config)
self.helper.listen(self._process_message)
def _process_message(self, data):
entity_id = data["entity_id"]
stix_object = self.helper.api.stix_cyber_observable.read(id=entity_id)
if not stix_object:
return "Observable not found"
observable_type = stix_object["entity_type"]
observable_value = stix_object.get("value", "")
enrichment_results = []
if observable_type == "IPv4-Addr":
enrichment_results = self._enrich_ip(observable_value, entity_id)
elif observable_type == "Domain-Name":
enrichment_results = self._enrich_domain(observable_value, entity_id)
if enrichment_results:
bundle = Bundle(objects=enrichment_results, allow_custom=True)
self.helper.send_stix2_bundle(bundle.serialize())
return "Enrichment completed"
def _enrich_ip(self, ip_address, entity_id):
"""Enrich IP address with GreyNoise, AbuseIPDB context."""
objects = []
# GreyNoise Community API
try:
gn_response = requests.get(
f"https://api.greynoise.io/v3/community/{ip_address}",
headers={"key": os.environ.get("GREYNOISE_API_KEY")},
timeout=30,
)
if gn_response.status_code == 200:
gn_data = gn_response.json()
classification = gn_data.get("classification", "unknown")
noise = gn_data.get("noise", False)
riot = gn_data.get("riot", False)
note_content = (
f"## GreyNoise Enrichment\n"
f"- Classification: {classification}\n"
f"- Internet Noise: {noise}\n"
f"- RIOT (Benign Service): {riot}\n"
f"- Name: {gn_data.get('name', 'N/A')}\n"
f"- Last Seen: {gn_data.get('last_seen', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"GreyNoise: {classification}",
allow_custom=True,
)
objects.append(note)
# Add labels based on classification
if classification == "malicious":
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:malicious"
)
elif riot:
self.helper.api.stix_cyber_observable.add_label(
id=entity_id, label_name="greynoise:benign-service"
)
except Exception as e:
self.helper.log_error(f"GreyNoise enrichment failed: {e}")
return objects
def _enrich_domain(self, domain, entity_id):
"""Enrich domain with WHOIS and DNS context."""
objects = []
try:
# Use SecurityTrails API for domain enrichment
st_response = requests.get(
f"https://api.securitytrails.com/v1/domain/{domain}",
headers={"APIKEY": os.environ.get("SECURITYTRAILS_API_KEY")},
timeout=30,
)
if st_response.status_code == 200:
st_data = st_response.json()
current_dns = st_data.get("current_dns", {})
a_records = [
r.get("ip") for r in current_dns.get("a", {}).get("values", [])
]
note_content = (
f"## SecurityTrails Enrichment\n"
f"- A Records: {', '.join(a_records)}\n"
f"- Alexa Rank: {st_data.get('alexa_rank', 'N/A')}\n"
f"- Hostname: {st_data.get('hostname', 'N/A')}"
)
note = Note(
content=note_content,
object_refs=[entity_id],
abstract=f"SecurityTrails: {domain}",
allow_custom=True,
)
objects.append(note)
except Exception as e:
self.helper.log_error(f"SecurityTrails enrichment failed: {e}")
return objects
if __name__ == "__main__":
connector = CustomEnrichmentConnector()
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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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Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
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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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