NITRO-IDS
Network IoT Traffic Robust Optimized Intrusion Detection System Using Deep Learning
NITRO-IDS is a graduation project developed to support intelligent monitoring and analysis of Internet of Things (IoT) network traffic using modern Artificial Intelligence technologies.
About the Project
About the Project
NITRO-IDS is an AI-powered cybersecurity project developed to analyze IoT network traffic and support intelligent intrusion detection through modern machine learning and deep learning techniques.
The project combines research, innovation, and practical implementation to provide a modern approach for understanding and monitoring IoT network environments.
Artificial Intelligence
IoT Network Monitoring
Cybersecurity Research
Research Publication
The research associated with NITRO-IDS was accepted and presented at the 16th International Conference for Innovations in Electronics, EM-Waves and Communications (ICEENG 2026), organized by the Military Technical College.
This publication represents an important milestone for the project and highlights the team's commitment to scientific research and innovation.
Presented at
ICEENG 2026
Conference Gallery
Highlights from ICEENG 2026
Project Supervisor
Project Supervisor
Dr. Amel Mohamed Mahmoud
Project Supervisor and Research Mentor. First author and supervisor of the NITRO-IDS publication presented at ICEENG 2026.
Meet the Team
The students behind NITRO-IDS
Acknowledgment
We would like to express our heartfelt gratitude to Dr. Amel Mohamed Mahmoud for her guidance, support, and mentorship throughout this project.
We also extend our sincere thanks to the Communications & Electronics Engineering Department and Canadian International College (CIC) for providing an inspiring academic environment that encouraged innovation, research, and professional growth.
Their support and dedication have been instrumental in the successful completion of this project.