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prayag-piya/README.md

πŸ‘‹ Hey there, I'm Prayag Piya

πŸ§‘β€πŸ’» Software Engineer | ML Engineer | Cybersecurity Developer

πŸ“ Toronto, Canada
πŸ“§ prayagpiya12@gmail.com
🌐 prayagpiya.com.np
πŸ”— LinkedIn β€’ GitHub


🧭 Mission

Building intelligent systems that defend, adapt, and respond to cyber threats autonomously.

I believe the future of cybersecurity lies in machine learning–driven defense, where systems not only detect attacks but learn and evolve with every threat.
My focus is on developing AI-assisted security tools β€” from incident response automation to graph-based intrusion detection using neural networks.


πŸ’‘ What I Do

  • 🧠 Machine Learning for Security – Applying deep learning and GNNs to model attacker behavior and network topology.
  • βš™οΈ Security Automation & SOAR – Building tools that automate detection, triage, and response workflows.
  • ☁️ Cloud & Infrastructure Security – Securing hybrid environments with SIEM, EDR, and continuous monitoring.
  • 🧩 DevSecOps – Embedding security into CI/CD pipelines using Docker, GitHub Actions, and Jenkins.

🧰 Core Skills

Category Tools & Technologies
Languages Python, Rust, C++, JavaScript, Bash
Frameworks / Libraries FastAPI, TensorFlow, PyTorch, React, scikit-learn
Security Stack ELK Stack, Bitdefender EDR, PCI-DSS, SIEM, Zabbix
Cloud & DevOps AWS, Azure, Docker, GitHub Actions, Jenkins
Databases MySQL, PostgreSQL, MongoDB

πŸš€ Recent Highlights

πŸ”Ή Hybrid Recommendation Engine – Thunder Bay Public Library

Built a large-scale collaborative + content-based recommendation system for over a million records, integrated with the library’s Sierra ILS through REST APIs and RabbitMQ.
Implemented full CI/CD automation with Docker and GitHub Actions.


πŸ”Ή SIEM Deployment & Threat Detection – DGhub Pvt. Ltd

Implemented a real-time ELK-based SIEM solution, aligned infrastructure with PCI-DSS, and deployed Bitdefender EDR to harden endpoints across the organization.


πŸ”Ή Fraud & Malware Detection with ML

Developed AI-based classification models for credit fraud and malware detection using TensorFlow and PyTorch to improve response accuracy and reduce false positives.


🧩 Current Research Focus

Exploring how Graph Neural Networks (GNNs) can map relationships between attack vectors, user behavior, and network events β€” enabling systems to anticipate and isolate threats before they spread.


πŸŽ“ Education

πŸŽ“ MSc, Computer Science – Lakehead University
πŸŽ“ Postgraduate in Cybersecurity – Centennial College
πŸŽ“ BSc (Hons), Computer Network & IT Security – London Metropolitan University


πŸ“« Connect with Me

If you’re interested in AI-driven security systems, automation in SOC operations, or graph-based threat modeling, I’d love to chat.

πŸ“§ prayagpiya12@gmail.com
πŸ”— LinkedIn


β€œThe next generation of cybersecurity isn’t just reactive β€” it’s predictive, autonomous, and intelligent.” πŸ”

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