- 🔐 Interested in Cybersecurity, AI Security, Privacy, and Applied Cryptography
- 🔭 Currently working on Security Automation, IAM, and Vulnerability Management
- 🧠 Exploring Explainable AI and Zero-Knowledge Proofs for Cybersecurity
- 🌱 Currently learning more about DevSecOps, Secure Software Engineering, and Access Governance
- 🧪 Building practical and isolated security labs using Python, Docker, and Linux
- 💬 Ask me about Python, Security Automation, IAM, XAI, and Cybersecurity Research
- 📫 Reach me at ntannguyen2004@gmail.com
- AI-assisted security operations
- Explainable intrusion detection
- AI security and trustworthy AI
- Privacy-preserving machine learning
- Zero-knowledge proofs
- Applied cryptography
- Identity and Access Management
- Vulnerability management
- Secure software engineering
- Cybersecurity automation
A Python-based security automation toolkit that converts vulnerability scan exports into structured and actionable security documentation.
The toolkit supports:
- Vulnerability normalization and deduplication
- Risk-register generation
- Remediation tracking
- Weekly security reporting
- Audit-ready evidence-pack generation
A lightweight simulation of Identity Governance and Administration workflows, including:
- Joiner, Mover, and Leaver processes
- Role-Based Access Control
- Access requests and approvals
- Periodic access reviews
- Audit-friendly documentation
- Stakeholder communication templates
A self-contained Docker laboratory for demonstrating safe and authorized credential validation.
The laboratory includes intentionally vulnerable and secured versions of:
- SSH
- FTP
- HTTP Basic Authentication
All services are isolated and exposed only through localhost. The project uses tiny lab-only wordlists and is designed strictly for ethical cybersecurity education and authorized local testing.
A research prototype combining machine learning, explainable artificial intelligence, and zero-knowledge proofs.
The prototype investigates how to verify that:
- A prediction was generated from a private network-flow input
- The explanation corresponds to the same hidden input
- Semantic-group feature attributions were calculated correctly
- Only selected outputs are publicly disclosed
Main technologies and methods:
- Logistic Regression
- Semantic-group Exact SHAP
- Circom
- Groth16
- IoT intrusion-detection data
A collection of system-hardening configurations, practical security exercises, and recommendations covering:
- Linux security
- Service exposure reduction
- Access control
- Firewall configuration
- Network-service enumeration
- Logging and monitoring
- Secure configuration documentation
A containerized Flask web application developed using Python and Docker.
The project demonstrates experience with:
- Backend development
- Web application architecture
- Database integration
- Containerization
- Application deployment
Cybersecurity · AI Security · Privacy · IAM · Applied Cryptography