Students of the BIT program presented a qualitative project titled “Fungi AI.” This intelligent system utilizes Deep Learning and Cloud Integration to discover and classify fungi into five distinct categories via a mobile application. The team included: Hudhaifa Sadiq, Ahmed Essam, Yassin Abdulaziz, Khalil Mohammed, and Suhail Hussein. This project exemplifies the university’s vision of leading digital transformation through practical AI solutions for medical and agricultural challenges.
Fungi AI: Mushroom Detection with Deep Learning
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IUTT Showcases "Rasid Logs Forensic": An Innovative Tool for Digital Forensics and Incident Response
15/04/2026
IUTT students develop “Rasid Logs Forensic”, a high-speed digital forensic platform for Windows log analysis and threat detection using Rust and Sigma rules.
IUTT Showcases "CyberTrap" Project: An Innovative Approach to Cyber Deception and Threat Detection
15/04/2026
IUTT students present “CyberTrap”, an innovative cyber deception project using Honeytokens to detect advanced threats and ensure immediate security response.
IUTT Showcases a Qualitative Project for a Real-Time Network Intrusion Prevention System Using Machine Learning
14/04/2026
IUTT students develop a cutting-edge Machine Learning-based Intrusion Prevention System (IPS) capable of processing 1.2M packets per second with 97% accuracy.