I build hands-on security projects around SOC operations, threat detection, AI-assisted security analytics, cloud security, DevSecOps, offensive security and reverse engineering.
- SOC & Detection Engineering — SIEM, threat detection, investigation, MITRE ATT&CK, threat hunting, SOAR, UEBA
- AI for Cybersecurity — ML-assisted prioritization, graph-based investigation, security analytics, code-agent security
- Cloud & DevSecOps — Azure, IAM/RBAC, Docker, CI/CD, secrets management, vulnerability automation
- Offensive Security — web/network pentesting, OWASP Top 10, controlled attack simulation
- Reverse Engineering — x86/x64, PE/ELF, Ghidra, IDA Free, x64dbg
End-to-end AI + cybersecurity project for IoT intrusion detection with reproducible ML pipelines, model tracking, API serving, monitoring and an analyst workbench.
Stack: CICIoT2023 · DVC · MLflow · FastAPI · Prometheus · Grafana · Airflow · Streamlit · GitHub Actions
SOC laboratory connecting Wazuh → Python enrichment → MITRE ATT&CK → UEBA/ML → Neo4j graph investigation → Streamlit → SOAR automation.
Highlights: 12 controlled attack scenarios · Atomic Red Team · Caldera · 1,400+ alerts · n8n/Shuffle · pfSense
Hands-on NSM environment using SELKS, Suricata, Elastic/Kibana, Scirius, Docker and Cisco traffic mirroring for intrusion detection and investigation.
Desktop security engineering project combining AES-CBC, Hamming(7,4), CRC, noisy-channel simulation, ACK/NACK retransmission and automated testing.
Defensive-security lab for Sigma, Wazuh and Suricata detections mapped to MITRE ATT&CK, with portable rules and a lightweight validation utility.
Applied lab for LLM / coding-agent security, prompt-injection evaluation, secure code-agent review and static checks for risky AI-generated Python patterns.
Cybersecurity · AI Security · Detection Engineering · Cloud Security · DevSecOps