Building a Real-Time Spyware Detection Engine with Flask & Machine Learning

Building a Real-Time Spyware Detection Engine

🔍 Introduction

Modern spyware evolves rapidly, requiring detection systems that combine static analysis with machine learning. This project implements a production-grade spyware scanner with:

Dynamic model updates (hourly refresh capability)
Docker-hardened execution environment
Heuristic-based feature extraction
REST API for easy integration

🛠 System Architecture

Core Components

Feature Extraction Engine
- PE file header analysis
- API call tracing
- Entropy-based anomaly detection

Machine Learning Pipeline

# Model loading with version control
class ModelManager:
    def load_model(self):
        self.model = joblib.load(self.model_path)
        self.metadata = self._load_metadata()
        logger.info(f"Loaded model v{self.metadata['version']}")

Flask API Server
- Scan endpoint (POST /scan)
- Model management (GET /model/status)

🔐 Security Hardening

Docker Best Practices

# Multi-stage build with non-root user
FROM python:3.9-slim AS builder
# ...
RUN useradd -m appuser && \
    chown -R appuser:appuser /app
USER appuser

Threat Analysis Features

Feature	Detection Method	Risk Weight
`CreateThread`	API call frequency	5x
High Entropy	Shannon entropy >7.5	3x
Hidden Registry	`RegSetValueEx` calls	4x

⚙️ Automated Model Updates

GitHub Integration

# Fetch latest model from GitHub Releases
MODEL_URL = os.getenv(
    "MODEL_URL",
    "https://github.com/.../releases/latest/download/model_release.tar.gz"
)

Version Control

// metadata.json
{
  "version": "20250324_223636",
  "metrics": {
    "accuracy": 0.96,
    "recall": 0.95
  }
}

📊 Detection Workflow

File Upload

curl -X POST http://localhost:5000/scan \
  -H "Content-Type: application/json" \
  -d '{"fileName":"test.exe", "fileContent":"<base64>"}'

Threat Analysis

def scan_file(file_stream):
    features = extract_features(file_stream)  # 2762-dim vector
    prediction = model.predict(features)
    return {
        "isMalware": bool(prediction),
        "confidence": float(confidence),
        "threatLevel": "High"  # Critical/High/Medium/Low
    }