Godwin Joe Dionisus

Completed CCNA-level training covering TCP/IP, subnetting, routing, switching, VLANs, and systematic troubleshooting. Gained exposure to CCNP-level concepts such as scalable network design, advanced routing protocols, and enterprise infrastructure planning. Developed strong foundations in network security, including authentication, access control, system hardening, and Windows Server administration. Applied Python for network automation, monitoring, and technical documentation.

Built a real-time ANPR surveillance system using YOLOv5 + OCR for security monitoring, access control, and incident logging. Implemented object tracking and secure data pipelines to improve reliability of vehicle identification in networked camera environments. Designed structured logging supporting audit trails, security analytics, and smart surveillance infrastructure. Strengthened understanding of AI-driven security systems, networked surveillance architecture, and data protection practices.

Developed predictive models with emphasis on secure data handling, anomaly detection, and data integrity. Processed large datasets using feature engineering and validation techniques relevant to threat detection and security analytics. Documented pipelines ensuring auditability, controlled access, and secure deployment practices.