Internet of Thing Network
Internet of Things (IoT) networks connect billions of devices, generating massive data streams and posing significant security and resource management challenges. Current research focuses on enhancing security through machine learning (ML) and deep learning (DL) models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and large language models (LLMs), for intrusion detection and attack prediction, often employing federated learning to address privacy concerns. These advancements are crucial for enabling reliable and secure operation of IoT networks across diverse applications, from smart homes and cities to industrial automation and healthcare.
Papers
Neuromorphic IoT Architecture for Efficient Water Management: A Smart Village Case Study
Mugdim Bublin, Heimo Hirner, Antoine-Martin Lanners, Radu Grosu
COMSPLIT: A Communication-Aware Split Learning Design for Heterogeneous IoT Platforms
Vukan Ninkovic, Dejan Vukobratovic, Dragisa Miskovic, Marco Zennaro
Resource Efficient Asynchronous Federated Learning for Digital Twin Empowered IoT Network
Shunfeng Chu, Jun Li, Jianxin Wang, Yiyang Ni, Kang Wei, Wen Chen, Shi Jin
Beyond Detection: Leveraging Large Language Models for Cyber Attack Prediction in IoT Networks
Alaeddine Diaf, Abdelaziz Amara Korba, Nour Elislem Karabadji, Yacine Ghamri-Doudane