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
Security Risks Concerns of Generative AI in the IoT
Honghui Xu, Yingshu Li, Olusesi Balogun, Shaoen Wu, Yue Wang, Zhipeng Cai
Distributed Swarm Learning for Edge Internet of Things
Yue Wang, Zhi Tian, FXin Fan, Zhipeng Cai, Cameron Nowzari, Kai Zeng
DeepHeteroIoT: Deep Local and Global Learning over Heterogeneous IoT Sensor Data
Muhammad Sakib Khan Inan, Kewen Liao, Haifeng Shen, Prem Prakash Jayaraman, Dimitrios Georgakopoulos, Ming Jian Tang