Demonstrated understanding of Windows 10/11 security and threat of continuous security training.
This is an AI-driven threat intelligence system that leverages machine learning algorithms to predict and prevent network security breaches. The system demonstrates superior performance in early threat detection compared to traditional methods.
Conducted thorough scans on a small internal network to identify open ports, potential vulnerabilities, and system misconfigurations.
Outcome: Produced detailed documentation with actionable recommendations for remediation.
Research project focused on developing machine learning-based lightweight IoT device profiling for enhanced security.
End-to-end security testing of web applications against the OWASP Top 10 — SQL injection, XSS, broken authentication and more.
Outcome: Delivered a prioritized report with proof-of-concept exploits and clear remediation steps.
Built a 24/7 monitoring pipeline that collects logs into a SIEM, correlates events and raises real-time alerts on suspicious activity.