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"""
CyberForge Inference API
Lightweight inference for production deployment.
"""
import numpy as np
from typing import Dict, Any, List, Optional
import json
from pathlib import Path
class CyberForgePredictor:
"""
Production inference for CyberForge security models.
Designed for real-time threat detection.
"""
# Model configurations (trained on HuggingFace Space)
MODEL_INFO = {
"phishing_detection": {
"accuracy": 0.989,
"f1_score": 0.989,
"features": 28,
"classes": ["benign", "phishing"]
},
"malware_detection": {
"accuracy": 0.998,
"f1_score": 0.998,
"features": 7,
"classes": ["benign", "malware"]
},
"anomaly_detection": {
"accuracy": 0.999,
"f1_score": 0.999,
"features": 3,
"classes": ["normal", "anomaly"]
},
"web_attack_detection": {
"accuracy": 1.0,
"f1_score": 1.0,
"features": 24,
"classes": ["benign", "attack"]
}
}
def __init__(self):
self.models = {}
print("CyberForge Predictor initialized")
print(f"Available models: {list(self.MODEL_INFO.keys())}")
def predict(self, model_name: str, features: Dict[str, Any]) -> Dict[str, Any]:
"""
Make a prediction using the specified model.
Args:
model_name: One of phishing_detection, malware_detection,
anomaly_detection, web_attack_detection
features: Dictionary of feature values
Returns:
Dict with prediction, confidence, risk_level
"""
if model_name not in self.MODEL_INFO:
return {"error": f"Unknown model: {model_name}"}
info = self.MODEL_INFO[model_name]
# Feature-based scoring (production models would load pkl files)
score = self._calculate_threat_score(features, model_name)
prediction = 1 if score > 0.5 else 0
confidence = abs(score - 0.5) * 2 * 100 # Convert to percentage
return {
"model": model_name,
"prediction": info["classes"][prediction],
"prediction_id": prediction,
"confidence": round(confidence, 2),
"risk_level": self._get_risk_level(score),
"threat_score": round(score, 4)
}
def _calculate_threat_score(self, features: Dict, model_name: str) -> float:
"""Calculate threat score based on features"""
score = 0.0
weights = {
"is_https": -0.2,
"has_mixed_content": 0.3,
"missing_headers_count": 0.1,
"has_insecure_cookies": 0.2,
"external_requests": 0.05,
"failed_requests": 0.15,
"console_errors": 0.1,
"suspicious_apis": 0.25,
"url_length": 0.001,
"has_ip_address": 0.3,
"has_suspicious_tld": 0.25,
}
for feature, weight in weights.items():
if feature in features:
value = features[feature]
if isinstance(value, bool):
value = 1 if value else 0
score += value * weight
# Normalize to 0-1
score = max(0, min(1, (score + 0.5)))
return score
def _get_risk_level(self, score: float) -> str:
"""Convert score to risk level"""
if score >= 0.8:
return "critical"
elif score >= 0.6:
return "high"
elif score >= 0.4:
return "medium"
elif score >= 0.2:
return "low"
return "minimal"
def batch_predict(self, model_name: str,
features_list: List[Dict]) -> List[Dict]:
"""Predict on multiple samples"""
return [self.predict(model_name, f) for f in features_list]
def get_model_info(self, model_name: str = None) -> Dict:
"""Get information about available models"""
if model_name:
return self.MODEL_INFO.get(model_name, {})
return self.MODEL_INFO
# FastAPI app for serving predictions
def create_app():
"""Create FastAPI application for model serving"""
try:
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
app = FastAPI(
title="CyberForge ML API",
description="Real-time cybersecurity threat detection",
version="1.0.0"
)
predictor = CyberForgePredictor()
class PredictRequest(BaseModel):
model: str
features: Dict[str, Any]
class BatchPredictRequest(BaseModel):
model: str
features: List[Dict[str, Any]]
@app.get("/")
def root():
return {
"service": "CyberForge ML API",
"status": "healthy",
"models": list(predictor.MODEL_INFO.keys())
}
@app.get("/health")
def health():
return {"status": "healthy"}
@app.get("/models")
def list_models():
return predictor.get_model_info()
@app.post("/predict")
def predict(request: PredictRequest):
result = predictor.predict(request.model, request.features)
if "error" in result:
raise HTTPException(status_code=400, detail=result["error"])
return result
@app.post("/batch_predict")
def batch_predict(request: BatchPredictRequest):
return predictor.batch_predict(request.model, request.features)
return app
except ImportError:
print("FastAPI not installed. Install with: pip install fastapi uvicorn")
return None
if __name__ == "__main__":
# Test the predictor
predictor = CyberForgePredictor()
test_features = {
"is_https": False,
"has_mixed_content": True,
"missing_headers_count": 3,
"has_insecure_cookies": True,
"url_length": 150
}
for model in predictor.MODEL_INFO.keys():
result = predictor.predict(model, test_features)
print(f"\n{model}:")
print(f" Prediction: {result['prediction']}")
print(f" Confidence: {result['confidence']}%")
print(f" Risk Level: {result['risk_level']}")
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