Designing Responsible and Efficient AI Systems for IoT Threat Detection and Retrieval-Augmented Educational Assistance

This project explores two critical areas of AI research: cybersecurity for Internet of Things (IoT) systems and responsible AI assistants for education and research. IoT devices, widely used in healthcare, smart cities, and industry, are highly vulnerable to cyberattacks due to limited resources and large-scale connectivity. We propose AI-based intrusion detection systems that are lightweight, scalable, and capable of identifying both known and unknown threats in real time, balancing accuracy with computational efficiency.

Simultaneously, the project develops AI assistants using Retrieval-Augmented Generation (RAG) to support education and research. These assistants combine generative models with external knowledge retrieval to provide accurate, traceable, and ethically responsible guidance. They address common challenges such as hallucinations, biases, and lack of transparency, embedding safeguards for privacy, fairness, and user trust.

By integrating secure IoT monitoring with responsible AI applications, this project advances both technology and society. It strengthens the protection of critical digital infrastructures while providing reliable, transparent AI tools that enhance learning, research, and knowledge management. The outcomes aim to foster innovation in digital health, education, and cybersecurity, demonstrating AI that is both effective and responsible.

Faculty Supervisor:

Plinio Pelegrini Morita

Student:

Partner:

ESIEE-IT

Discipline:

Computer science

Sector:

Artificial Intelligence; Cyber Security; Health and Related Sciences and Technology

University:

University of Waterloo

Program: