Edge-AI-Driven Real-Time Clustering for Threat Detection in 6G Networks

Emilio Paolini (Scuola St.'Anna, IT)

Abstract

In the context of 6G network security, the deployment of AI-driven models for threat detection at the edge is emerging as a pivotal strategy to enhance real-time response and efficiency. This workshop discusses an unsupervised deep learning model, specifically designed for the autonomous detection and mitigation of DoS attacks, highlighting its significance in the fast-paced environment of 6G communications.

Curriculum Vitae

Emilio Paolini is a PhD student at Scuola Superiore Sant'Anna, focusing on improving AI systems at the edge of computer networks. His research includes developing real-time AI algorithms for threat detection in 6G networks and accelerating AI with photonic technology, aiming to enhance efficiency and responsiveness in edge computing.