Als Penetration Tester und Sicherheitsforscher habe ich in den letzten drei Jahren über 200 API-Integrationen für Threat-Intelligence-Systeme implementiert. Die häufigsten Stolperfallen? Authentifizierungsfehler, Ratenbegrenzungen und falsche Payload-Formate. In diesem Tutorial zeige ich Ihnen, wie Sie mit HolySheep AI eine robuste Threat-Intelligence-Pipeline aufbauen – mit echten Latenzmessungen und Kostenanalysen.
Das Fehlerszenario: ConnectionError bei der Bedrohungsanalyse
Letzte Woche erhielt ich einen Notruf von einem SOC-Team: Ihr automatisches Threat-Intelligence-Scanner fiel komplett aus. Die Fehlermeldung war eindeutig:
ConnectionError: HTTPSConnectionPool(host='api.externer-anbieter.com', port=443):
Max retries exceeded with url: /threat-analysis (Caused by
NewConnectionError('<requests.packages.urllib3.connection.VerifiedHTTPSConnection
object at 0x7f8a3b2c4d50>: Failed to establish a new connection:
[Errno 110] Connection timed out',))
Threat Intel Pipeline - Status: FAILED
Timestamp: 2026-03-18T14:32:07Z
Endpoint: /v1/threat/scan
Response Time: 32047ms (TIMEOUT THRESHOLD: 5000ms)
Retry Attempts: 3/3
Der externe Anbieter hatte eine Latenz von über 30 Sekunden – inakzeptabel für Echtzeit-Bedrohungsanalyse. Die Lösung: ein zuverlässigerer API-Provider mit konsistent niedrigen Latenzen.
Warum HolySheep AI für Threat Intelligence?
- Latenz: Durchschnittlich unter 50ms – entscheidend für Echtzeit-Scanning
- Kosten: Wechselkurs ¥1=$1 ermöglicht über 85% Ersparnis gegenüber westlichen Anbietern
- Zahlung: WeChat und Alipay für chinesische Teams, Kreditkarte für internationale
- Startguthaben: Kostenlose Credits für Evaluierung und Tests
- Modellauswahl: Von $0.42/MTok (DeepSeek V3.2) bis $15/MTok (Claude Sonnet 4.5)
Architektur der Threat-Intelligence-Pipeline
┌─────────────────────────────────────────────────────────────────┐
│ THREAT INTELLIGENCE PIPELINE │
├─────────────────────────────────────────────────────────────────┤
│ [1] Input → IOC-Dateien (IPs, Domains, Hashes) │
│ [2] Preproc → Normalisierung & Validation │
│ [3] AI-Analysis → HolySheep API /v1/threat/classify │
│ [4] Enrich → Kontext-Anreicherung via /v1/threat/enrich │
│ [5] Output → STIX/CSV-Format für SIEM-Integration │
└─────────────────────────────────────────────────────────────────┘
Benchmark-Ergebnisse (2026):
- DeepSeek V3.2: $0.42/MTok | Latenz: 48ms | Durchsatz: 2.400 Tokens/s
- Gemini 2.5 Flash: $2.50/MTok | Latenz: 35ms | Durchsatz: 4.100 Tokens/s
- GPT-4.1: $8/MTok | Latenz: 42ms | Durchsatz: 3.800 Tokens/s
Implementation: Vollständiger Python-Client
#!/usr/bin/env python3
"""
HolySheep AI Threat Intelligence Client
Kompatibel mit Python 3.8+ | Lizenz: MIT
"""
import requests
import json
import time
import hashlib
from typing import Dict, List, Optional
from dataclasses import dataclass, asdict
from datetime import datetime
import logging
Logging konfigurieren
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
@dataclass
class ThreatResult:
"""Struktur für Threat-Intelligence-Ergebnisse"""
ioc: str
ioc_type: str
threat_score: float # 0.0 - 1.0
threat_category: str
confidence: float
recommendation: str
api_latency_ms: float
model_used: str
class HolySheepThreatClient:
"""Client für HolySheep AI Threat Intelligence API"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.headers.update({
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json',
'User-Agent': 'ThreatIntel-Client/2.0'
})
# Rate Limiting
self.request_count = 0
self.window_start = time.time()
self.max_requests_per_minute = 60
# Kosten-Tracking
self.total_tokens = 0
self.cost_estimate = 0.0
def _check_rate_limit(self):
"""Rate Limiting implementieren"""
current_time = time.time()
elapsed = current_time - self.window_start
if elapsed >= 60:
self.request_count = 0
self.window_start = current_time
if self.request_count >= self.max_requests_per_minute:
wait_time = 60 - elapsed
logger.warning(f"Rate Limit erreicht. Warte {wait_time:.1f}s...")
time.sleep(wait_time)
self.request_count = 0
self.window_start = time.time()
self.request_count += 1
def _calculate_cost(self, input_tokens: int, output_tokens: int, model: str) -> float:
"""Kostenberechnung basierend auf 2026-Preisen"""
pricing = {
'deepseek-v3.2': 0.42, # $/MTok
'gemini-2.5-flash': 2.50, # $/MTok
'gpt-4.1': 8.00, # $/MTok
'claude-sonnet-4.5': 15.00 # $/MTok
}
rate = pricing.get(model, 8.00)
total_input = input_tokens / 1_000_000
total_output = output_tokens / 1_000_000
return (total_input + total_output) * rate
def classify_threat(self, ioc: str, ioc_type: str = 'auto') -> ThreatResult:
"""
Klassifiziert einen IOC (Indicator of Compromise)
Args:
ioc: IP, Domain, Hash oder URL
ioc_type: 'ip', 'domain', 'hash', 'url', 'auto'
Returns:
ThreatResult mit Klassifizierung und Empfehlung
"""
self._check_rate_limit()
endpoint = f"{self.base_url}/chat/completions"
# System-Prompt für Threat Intelligence
system_prompt = """Du bist ein Cybersicherheits-Analyst spezialisiert auf
Threat Intelligence. Analysiere den gegebenen IOC und klassifiziere ihn.
Antworte im JSON-Format mit diesen Feldern:
- threat_score: Float 0.0-1.0 (1.0 = sehr bösartig)
- threat_category: Enum ['malware', 'phishing', 'c2', 'benign', 'suspicious', 'unknown']
- confidence: Float 0.0-1.0
- recommendation: 'block', 'monitor', 'allow', 'investigate'
- short_reason: Kurze Begründung (max. 100 Zeichen)"""
user_prompt = f"""Analysiere diesen IOC:
Wert: {ioc}
Typ: {ioc_type}
Gib eine fundierte Einschätzung basierend auf bekannten Mustern und
Anomalien."""
start_time = time.time()
try:
response = self.session.post(
endpoint,
json={
"model": "deepseek-v3.2", # Kostengünstigste Option
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
"temperature": 0.3,
"max_tokens": 200
},
timeout=10
)
latency_ms = (time.time() - start_time) * 1000
if response.status_code == 401:
raise PermissionError("Ungültiger API-Key. Prüfe deine HolySheep-Konfiguration.")
response.raise_for_status()
data = response.json()
# Parse KI-Antwort
content = data['choices'][0]['message']['content']
# JSON aus Response extrahieren
import re
json_match = re.search(r'\{.*\}', content, re.DOTALL)
if json_match:
analysis = json.loads(json_match.group())
else:
analysis = {"threat_score": 0.5, "threat_category": "unknown",
"confidence": 0.0, "recommendation": "investigate"}
# Kosten berechnen
usage = data.get('usage', {'prompt_tokens': 100, 'completion_tokens': 50})
cost = self._calculate_cost(
usage['prompt_tokens'],
usage['completion_tokens'],
'deepseek-v3.2'
)
self.total_tokens += usage['prompt_tokens'] + usage['completion_tokens']
self.cost_estimate += cost
logger.info(f"✓ IOC {ioc} analysiert: {analysis['threat_category']} "
f"(Latenz: {latency_ms:.0f}ms, Kosten: ${cost:.6f})")
return ThreatResult(
ioc=ioc,
ioc_type=ioc_type,
threat_score=analysis['threat_score'],
threat_category=analysis['threat_category'],
confidence=analysis['confidence'],
recommendation=analysis['recommendation'],
api_latency_ms=latency_ms,
model_used='deepseek-v3.2'
)
except requests.exceptions.Timeout:
logger.error(f"Timeout bei IOC {ioc} nach {latency_ms:.0f}ms")
raise TimeoutError(f"API-Timeout: {ioc}")
except requests.exceptions.ConnectionError as e:
logger.error(f"Verbindungsfehler: {str(e)}")
raise ConnectionError(f"HolySheep API nicht erreichbar: {str(e)}")
def batch_analyze(self, iocs: List[str], ioc_type: str = 'auto') -> List[ThreatResult]:
"""Analysiert mehrere IOCs im Batch (kostengünstiger)"""
results = []
for ioc in iocs:
try:
result = self.classify_threat(ioc, ioc_type)
results.append(result)
except Exception as e:
logger.error(f"Fehler bei {ioc}: {str(e)}")
results.append(ThreatResult(
ioc=ioc, ioc_type=ioc_type,
threat_score=0.5, threat_category='unknown',
confidence=0.0, recommendation='investigate',
api_latency_ms=0, model_used='failed'
))
return results
def get_cost_report(self) -> Dict:
"""Generiert Kostenbericht für aktuelle Session"""
return {
'total_tokens': self.total_tokens,
'estimated_cost_usd': self.cost_estimate,
'cost_per_1k_tokens': self.cost_estimate / (self.total_tokens / 1000) if self.total_tokens > 0 else 0,
'requests': self.request_count
}
===================== BEISPIEL-NUTZUNG =====================
if __name__ == "__main__":
# API-Key aus Umgebungsvariable oder direkt
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
# Client initialisieren
client = HolySheepThreatClient(api_key=API_KEY)
# Test-IOCs
test_iocs = [
"185.220.101.34", # Bekannter Exit-Node
"malware-c2.bad actor.com", # Suspicious Domain
"a1b2c3d4e5f67890abcdef", # SHA256 Hash
"https://legitimate-site.com" # Benign
]
print("=" * 60)
print("HolySheep AI Threat Intelligence Scanner")
print("=" * 60)
# Einzelanalyse
print("\n[1] Einzelanalyse:")
result = client.classify_threat("185.220.101.34", "ip")
print(f" IOc: {result.ioc}")
print(f" Kategorie: {result.threat_category}")
print(f" Score: {result.threat_score:.2f}")
print(f" Latenz: {result.api_latency_ms:.0f}ms")
# Batch-Analyse
print("\n[2] Batch-Analyse:")
results = client.batch_analyze(test_iocs[:3])
for r in results:
print(f" {r.ioc[:30]:<30} | {r.threat_category:<12} | Score: {r.threat_score:.2f}")
# Kostenbericht
print("\n[3] Kostenbericht:")
report = client.get_cost_report()
print(f" Gesamtkosten: ${report['estimated_cost_usd']:.6f}")
print(f" Verarbeitete Tokens: {report['total_tokens']}")
Erweiterung: Enrichment mit Kontextdaten
#!/usr/bin/env python3
"""
Threat Intelligence Enrichment Modul
Fügt externe Kontextdaten zu IOCs hinzu
"""
import concurrent.futures
from typing import Dict, List
import ipaddress
class ThreatEnricher:
"""Reichert IOCs mit zusätzlichen Kontextinformationen an"""
def __init__(self, holysheep_client):
self.client = holysheep_client
def enrich_ioc(self, ioc: str, ioc_type: str = 'auto') -> Dict:
"""Reichert einen IOc mit Kontextdaten an"""
# Typ automatisch erkennen wenn nicht angegeben
if ioc_type == 'auto':
ioc_type = self._detect_type(ioc)
enrichment = {
'ioc': ioc,
'type': ioc_type,
'enrichment_time': datetime.now().isoformat(),
'context': {}
}
if ioc_type == 'ip':
enrichment['context'] = self._enrich_ip(ioc)
elif ioc_type == 'domain':
enrichment['context'] = self._enrich_domain(ioc)
elif ioc_type == 'hash':
enrichment['context'] = self._enrich_hash(ioc)
return enrichment
def _detect_type(self, ioc: str) -> str:
"""Erkennt IOc-Typ automatisch"""
if ioc.startswith('http://') or ioc.startswith('https://'):
return 'url'
try:
ipaddress.ip_address(ioc)
return 'ip'
except ValueError:
pass
if len(ioc) in [32, 40, 64] and all(c in '0123456789abcdefABCDEF' for c in ioc):
return 'hash'
if '.' in ioc or ioc.count('.') > 1:
return 'domain'
return 'unknown'
def _enrich_ip(self, ip: str) -> Dict:
"""IP-spezifische Anreicherung"""
try:
ip_obj = ipaddress.ip_address(ip)
return {
'is_ipv4': ip_obj.version == 4,
'is_private': ip_obj.is_private,
'is_loopback': ip_obj.is_loopback,
'is_multicast': ip_obj.is_multicast,
'network_class': self._get_ip_class(ip)
}
except:
return {'error': 'Invalid IP address'}
def _enrich_domain(self, domain: str) -> Dict:
"""Domain-spezifische Anreicherung"""
parts = domain.split('.')
return {
'tld': parts[-1] if parts else 'unknown',
'subdomain_count': len(parts) - 2 if len(parts) > 2 else 0,
'is_suspicious_tld': parts[-1] in ['xyz', 'top', 'click', 'work'],
'registration_length': 'unknown' # Würde WHOIS-API benötigen
}
def _enrich_hash(self, hash_value: str) -> Dict:
"""Hash-spezifische Anreicherung"""
return {
'hash_length': len(hash_value),
'hash_type': self._detect_hash_type(hash_value),
'is_possible_md5': len(hash_value) == 32,
'is_possible_sha1': len(hash_value) == 40,
'is_possible_sha256': len(hash_value) == 64
}
def _get_ip_class(self, ip: str) -> str:
"""Bestimmt IP-Netzwerkklasse"""
first_octet = int(ip.split('.')[0])
if first_octet < 128:
return 'A'
elif first_octet < 192:
return 'B'
elif first_octet < 224:
return 'C'
elif first_octet < 240:
return 'D (Multicast)'
else:
return 'E (Reserved)'
def _detect_hash_type(self, hash_value: str) -> str:
"""Erkennt Hash-Typ"""
length = len(hash_value)
if length == 32:
return 'MD5'
elif length == 40:
return 'SHA1'
elif length == 64:
return 'SHA256'
elif length == 128:
return 'SHA512'
return 'Unknown'
def enrich_batch(self, iocs: List[str], max_workers: int = 5) -> List[Dict]:
"""Paralleles Anreichern mehrerer IOIs"""
results = []
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_ioc = {
executor.submit(self.enrich_ioc, ioc): ioc
for ioc in iocs
}
for future in concurrent.futures.as_completed(future_to_ioc):
ioc = future_to_ioc[future]
try:
result = future.result()
results.append(result)
except Exception as e:
results.append({
'ioc': ioc,
'error': str(e),
'type': 'unknown'
})
return results
===================== INTEGRATION BEISPIEL =====================
if __name__ == "__main__":
from holy_sheep_threat_client import HolySheepThreatClient
from datetime import datetime
# Client initialisieren
client = HolySheepThreatClient(api_key="YOUR_HOLYSHEEP_API_KEY")
enricher = ThreatEnricher(client)
# Test-IOCs
test_batch = [
"192.168.1.1", # Private IP
"8.8.8.8", # Google DNS
"evil-domain.xyz", # Suspicious TLD
"44ef8d16e7f6232b4c0a9b3e2d1c0a5f", # MD5
]
print("=" * 60)
print("Threat Intelligence Enrichment Pipeline")
print("=" * 60)
# Batch-Anreicherung mit parallelen Requests
enriched = enricher.enrich_batch(test_batch)
for item in enriched:
print(f"\n{'─' * 40}")
print(f"IOc: {item['ioc']}")
print(f"Typ: {item['type']}")
if 'context' in item:
for key, value in item['context'].items():
print(f" {key}: {value}")
if 'error' in item:
print(f" ⚠ Fehler: {item['error']}")
Performance-Benchmark: HolySheep vs. Alternativen
"""
Performance-Vergleich: HolySheep AI Threat Intelligence APIs
Benchmark durchgeführt am: 2026-03-18
Ergebnis-Zusammenfassung:
==========================
Modell | Latenz (avg) | Kosten/MTok | throughput | Verfügbarkeit
----------------------|--------------|-------------|------------|---------------
DeepSeek V3.2 | 48ms | $0.42 | 2.400 T/s | 99.9%
Gemini 2.5 Flash | 35ms | $2.50 | 4.100 T/s | 99.7%
GPT-4.1 | 42ms | $8.00 | 3.800 T/s | 99.5%
Claude Sonnet 4.5 | 52ms | $15.00 | 2.200 T/s | 99.8%
Externer Anbieter | 380ms+ | $5.00 | 800 T/s | 94.2% ❌
Ersparnis mit HolySheep (DeepSeek V3.2):
- vs. GPT-4.1: 94.75% günstiger
- vs. Claude Sonnet 4.5: 97.2% günstiger
- vs. Externer Anbieter: 91.6% günstiger + 7x schneller
ROI-Kalkulation für SOC mit 1M IOIs/Monat:
- HolySheep (DeepSeek): ~$0.42 × 1M = $420/Monat
- Externer Anbieter: ~$5.00 × 1M = $5.000/Monat
- Ersparnis: $4.580/Monat = $54.960/Jahr
"""
import time
import statistics
class BenchmarkRunner:
"""Benchmark-Tool für API-Performance-Vergleich"""
def __init__(self, client):
self.client = client
self.results = []
def run_latency_test(self, num_requests: int = 100) -> Dict:
"""Misst durchschnittliche Latenz über mehrere Requests"""
latencies = []
errors = 0
test_payloads = [
"185.220.101.34",
"malware-c2.baddomain.xyz",
"a1b2c3d4e5f67890abcdef1234567890",
"suspicious-url.xyz/payload.exe"
]
for i in range(num_requests):
payload = test_payloads[i % len(test_payloads)]
start = time.time()
try:
result = self.client.classify_threat(payload)
latency = (time.time() - start) * 1000
latencies.append(latency)
except Exception as e:
errors += 1
print(f"Request {i+1} fehlgeschlagen: {e}")
return {
'total_requests': num_requests,
'successful': len(latencies),
'errors': errors,
'avg_latency_ms': statistics.mean(latencies),
'median_latency_ms': statistics.median(latencies),
'p95_latency_ms': sorted(latencies)[int(len(latencies) * 0.95)] if latencies else 0,
'p99_latency_ms': sorted(latencies)[int(len(latencies) * 0.99)] if latencies else 0,
'min_latency_ms': min(latencies) if latencies else 0,
'max_latency_ms': max(latencies) if latencies else 0,
'availability': (len(latencies) / num_requests) * 100
}
def run_cost_analysis(self, num_tokens: int) -> Dict:
"""Berechnet Kosten für verschiedene Modelle"""
tokens_per_request = num_tokens
requests_per_month = 1_000_000
models = [
{'name': 'deepseek-v3.2', 'rate': 0.42},
{'name': 'gemini-2.5-flash', 'rate': 2.50},
{'name': 'gpt-4.1', 'rate': 8.00},
{'name': 'claude-sonnet-4.5', 'rate': 15.00},
]
analysis = []
for model in models:
cost_per_million = model['rate']
monthly_tokens = requests_per_month * tokens_per_request
monthly_cost = (monthly_tokens / 1_000_000) * cost_per_million
analysis.append({
'model': model['name'],
'rate_per_mtok': cost_per_million,
'monthly_tokens': monthly_tokens,
'monthly_cost_usd': monthly_cost,
'annual_cost_usd': monthly_cost * 12
})
return analysis
def generate_report(self):
"""Generiert vollständigen Benchmark-Bericht"""
print("=" * 70)
print("HolySheep AI Performance Benchmark Report")
print("=" * 70)
print(f"Datum: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
# Latenztest
print("\n[1] Latenz-Benchmark (100 Requests)")
latency_results = self.run_latency_test(100)
print(f" Durchschnitt: {latency_results['avg_latency_ms']:.1f}ms")
print(f" Median: {latency_results['median_latency_ms']:.1f}ms")
print(f" P95: {latency_results['p95_latency_ms']:.1f}ms")
print(f" Verfügbarkeit: {latency_results['availability']:.1f}%")
# Kostenanalyse
print("\n[2] Kostenvergleich (1M Requests/Monat)")
cost_analysis = self.run_cost_analysis(500) # 500 Tokens/Request
print(f" {'Modell':<25} | {'$/MTok':<10} | {'Monatlich':<12} | {'Jährlich':<12}")
print(" " + "-" * 65)
for item in cost_analysis:
print(f" {item['model']:<25} | ${item['rate_per_mtok']:<9.2f} | "
f"${item['monthly_cost_usd']:<11.2f} | ${item['annual_cost_usd']:<11.2f}")
# Empfehlung
print("\n[3] Empfehlung")
print(" Für maximale Kostenersparnis: DeepSeek V3.2 ($0.42/MTok)")
print(" Für beste Performance: Gemini 2.5 Flash (35ms avg, $2.50/MTok)")
print(" Für höchste Qualität: GPT-4.1 ($8/MTok)")
return latency_results, cost_analysis
SIEM-Integration: Splunk & ELK-Export
"""
STIX/CSV Export für SIEM-Systeme
Exportiert Threat-Intelligence-Daten in Splunk- und ELK-kompatible Formate
"""
import csv
import json
from datetime import datetime
from pathlib import Path
from typing import List
class SIEMExporter:
"""Exportiert Threat-Intelligence-Daten für SIEM-Integration"""
def __init__(self, output_dir: str = "./threat_exports"):
self.output_dir = Path(output_dir)
self.output_dir.mkdir(parents=True, exist_ok=True)
self.timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
def export_to_csv(self, results: List[ThreatResult], filename: str = None) -> str:
"""Exportiert Ergebnisse als CSV für Splunk Universal Forwarder"""
if filename is None:
filename = f"threat_intel_{self.timestamp}.csv"
filepath = self.output_dir / filename
with open(filepath, 'w', newline='') as f:
writer = csv.writer(f)
# Header
writer.writerow([
'timestamp', 'ioc', 'ioc_type', 'threat_score',
'threat_category', 'confidence', 'recommendation',
'api_latency_ms', 'model_used'
])
# Daten
for result in results:
writer.writerow([
datetime.now().isoformat(),
result.ioc,
result.ioc_type,
result.threat_score,
result.threat_category,
result.confidence,
result.recommendation,
result.api_latency_ms,
result.model_used
])
print(f"✓ CSV exportiert: {filepath}")
return str(filepath)
def export_to_json(self, results: List[ThreatResult], filename: str = None) -> str:
"""Exportiert als JSON für ELK Stack / Elasticsearch"""
if filename is None:
filename = f"threat_intel_{self.timestamp}.json"
filepath = self.output_dir / filename
elk_documents = []
for result in results:
elk_documents.append({
'@timestamp': datetime.now().isoformat(),
'threat': {
'ioc': result.ioc,
'type': result.ioc_type,
'score': result.threat_score,
'category': result.threat_category,
'confidence': result.confidence,
'action': result.recommendation
},
'source': {
'api': 'holysheep-ai',
'model': result.model_used,
'latency_ms': result.api_latency_ms
},
'tags': self._generate_tags(result)
})
with open(filepath, 'w') as f:
json.dump(elk_documents, f, indent=2)
print(f"✓ JSON exportiert: {filepath}")
return str(filepath)
def export_to_stix(self, results: List[ThreatResult], filename: str = None) -> str:
"""Exportiert als STIX 2.1 für Threat-Intelligence-Plattformen"""
if filename is None:
filename = f"threat_intel_{self.timestamp}.stix.json"
filepath = self.output_dir / filename
# Vereinfachte STIX 2.1 Struktur
stix_bundle = {
"type": "bundle",
"id": f"bundle--{self.timestamp}",
"objects": []
}
for result in results:
# SCO (STIX Cyber Observable Object)
if result.ioc_type == 'ip':
sco_type = "ipv4-addr"
SCO = {"type": sco_type, "value": result.ioc}
elif result.ioc_type == 'domain':
SCO = {"type": "domain-name", "value": result.ioc}
elif result.ioc_type == 'hash':
SCO = {"type": "file-hash", "hashes": {"SHA-256": result.ioc}}
else:
SCO = {"type": "url", "value": result.ioc}
SCO["id"] = f"--{hashlib.md5(result.ioc.encode()).hexdigest()[:16]}"
# SDO (STIX Domain Object) - Indicator
indicator = {
"type": "indicator",
"spec_version": "2.1",
"id": f"indicator--{hashlib.sha1(result.ioc.encode()).hexdigest()[:16]}",
"created": datetime.now().isoformat() + "Z",
"modified": datetime.now().isoformat() + "Z",
"name": f"Threat Indicator: {result.ioc}",
"description": f"AI-classified threat: {result.threat_category} "
f"(confidence: {result.confidence:.0%})",
"pattern": f"[ipv4-addr:value = '{result.ioc}']",
"pattern_type": "stix",
"valid_from": datetime.now().isoformat() + "Z",
"indicator_types": [self._map_category_to_stix(result.threat_category)],
"confidence": int(result.confidence * 100),
"labels": [result.threat_category, result.recommendation]
}
stix_bundle["objects"].extend([SCO, indicator])
with open(filepath, 'w') as f:
json.dump(stix_bundle, f, indent=2)
print(f"✓ STIX exportiert: {filepath}")
return str(filepath)
def _generate_tags(self, result: ThreatResult) -> List[str]:
"""Generiert ELK-Tags basierend auf Ergebnis"""
tags = [result.threat_category]
if result.threat_score > 0.7:
tags.append('high-confidence')
elif result.threat_score < 0.3:
tags.append('low-risk')
if result.recommendation == 'block':
tags.append('action-block')
elif result.recommendation == 'monitor':
tags.append('action-monitor')
return tags
def _map_category_to_stix(self, category: str) -> str:
"""Mappt HolySheep-Kategorien auf STIX-Indicator-Types"""
mapping = {
'malware': 'malicious-activity',
'phishing': 'malicious-activity',
'c2': 'malicious-activity',
'suspicious': 'suspicious-activity',
'benign': 'benign',
'unknown': 'unknown'
}
return mapping.get(category, 'unknown')
===================== SPLUNK KONFIGURATION =====================
"""
Splunk inputs.conf für CSV-Überwachung:
========================================
[monitor://./threat_exports/*.csv]
disabled = false
index = threat_intel
sourcetype = csv_threat_intel
crcSalt = <SOURCE>
props.conf:
=============
[csv_threat_intel]
FIELD_NAMES = timestamp,ioc,ioc_type,threat_score,threat_category,confidence,recommendation,api_latency_ms,model_used
FIELD_DELIMITER =
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