🚀 Building a Real-Time Virus Scanning System Using ML: Lessons Learned
Introduction
In this blog, we’ll walk through our journey of designing and developing a real-time, scalable virus scanning system that leverages machine learning (ML) to detect malware. We’ll cover key challenges, solutions, and what we’ve learned along the way.
🔗 GitHub Repository: Repo Link
1️⃣ Understanding the Problem
Traditional virus scanning methods rely on signature-based detection, which can be bypassed by advanced threats. Our goal was to design a modern, scalable system that:
✅ Allows users to upload files for scanning.
✅ Uses multiple virus scanners along with an ML model.
✅ Supports real-time analysis while handling large-scale traffic.
2️⃣ Designing the Architecture
📌 Key Components
- API Gateway: Manages authentication and routes requests.
- File Storage: Temporarily holds uploaded files.
- Scanning Service: Runs virus scans in an isolated environment (VMs, Docker).
- ML Engine: Detects malware using file behavior analysis.
- Result Processing: Aggregates scan reports and stores them in a database.
- Monitoring & Security: Logs system activity and ensures reliability.
🛠️ Tech Stack
- Backend: TypeScript (Node.js + Express)
- ML Model: Python (TensorFlow/PyTorch)
- Database: PostgreSQL / MongoDB
- Storage: S3-compatible cloud storage
- Security: JWT authentication, rate limiting, sandboxing
3️⃣ Challenges and How We Solved Them
| Challenge | Solution |
|---|---|
| Isolating the scanning process | Used Docker & VMs for sandboxed execution. |
| Handling large files (50KB – 2GB) | Implemented streaming uploads and chunk-based processing. |
| Scalability for millions of users | Designed a distributed system using asynchronous processing. |
| Ensuring security | Implemented rate limiting, access control, and monitoring. |
4️⃣ Key Takeaways
🔑 Key Insight:
“Building a real-time, scalable virus scanning system requires a secure, isolated environment for processing untrusted files, leveraging asynchronous and distributed architectures to handle large-scale traffic efficiently, while integrating ML models to enhance threat detection beyond traditional signature-based methods.”
5️⃣ Next Steps
🎯 Enhancements We Plan to Add:
✅ Threat intelligence integration (real-time updates from external sources).
✅ Better ML models with improved accuracy.
✅ Auto-sandboxing for unknown threats.
Conclusion
Building this system has been a huge learning experience in backend development, ML integration, and security. If you’re interested in contributing, check out our GitHub repository! 🚀
🔖 Tags:
#MachineLearning #CyberSecurity #VirusScanning #AIForSecurity #ThreatDetection #BackendDevelopment #NodeJS #Python #RealTimeProcessing #ScalableSystems
Let me know if you’d like any refinements! 🚀🔥