Internet of Thing
The Internet of Things (IoT) involves connecting billions of devices to collect and analyze data, aiming to improve efficiency and decision-making across various sectors. Current research heavily focuses on enhancing IoT security through machine learning (ML) models like deep neural networks (CNNs, LSTMs, Transformers), federated learning, and the integration of large language models (LLMs) for improved anomaly detection and attack prediction. These advancements are crucial for addressing the growing concerns of data privacy, security vulnerabilities, and resource constraints within increasingly complex IoT networks, impacting fields from smart cities to healthcare.
Papers
VHetNets for AI and AI for VHetNets: An Anomaly Detection Case Study for Ubiquitous IoT
Weili Wang, Omid Abbasi, Halim Yanikomeroglu, Chengchao Liang, Lun Tang, Qianbin Chen
A Lightweight Moving Target Defense Framework for Multi-purpose Malware Affecting IoT Devices
Jan von der Assen, Alberto Huertas Celdrán, Pedro Miguel Sánchez Sánchez, Jordan Cedeño, Gérôme Bovet, Gregorio Martínez Pérez, Burkhard Stiller
Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature
Rahnuma Mahzabin, Fahim Hossain Sifat, Sadia Anjum, Al-Akhir Nayan, Muhammad Golam Kibria
IoT based Smart Water Quality Prediction for Biofloc Aquaculture
Md. Mamunur Rashid, Al-Akhir Nayan, Md. Obaidur Rahman, Sabrina Afrin Simi, Joyeta Saha, Muhammad Golam Kibria
Topological Simplification of Signals for Inference and Approximate Reconstruction
Gary Koplik, Nathan Borggren, Sam Voisin, Gabrielle Angeloro, Jay Hineman, Tessa Johnson, Paul Bendich
Service Discovery in Social Internet of Things using Graph Neural Networks
Aymen Hamrouni, Hakim Ghazzai, Yehia Massoud