Real-Time Anomaly Detection in IoT Networks Using Edge AI and Advanced Data Science Techniques

EasyChair Preprint 15377

Abstract

As the Internet of Things (IoT) expands across various sectors, from healthcare to industrial automation, the need for real-time anomaly detection has become paramount for ensuring data security and network integrity. Edge AI, combined with advanced data science techniques, provides a robust solution for real-time analysis by processing data close to the source, reducing latency and enhancing security. This paper explores the application of anomaly detection algorithms in IoT networks using Edge AI, discussing frameworks that allow decentralized anomaly identification and data processing at the edge. Case studies highlight the effectiveness of these techniques in detecting and mitigating potential threats in IoT systems.

Keyphrases: Data Science, Edge AI, IoT networks, Security, Threat Mitigation, decentralized processing, real-time anomaly detection

@booklet{EasyChair:15377,
  author    = {Matilda Bennett},
  title     = {Real-Time Anomaly Detection in IoT Networks Using Edge AI and Advanced Data Science Techniques},
  howpublished = {EasyChair Preprint 15377},
  year      = {EasyChair, 2024}}