Distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach

arXiv:2405.08755v1 Announce Type: new
Abstract: With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decentralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning techniques such as the in-context learning feature of large language models (LLMs), represents a promising paradigm for enhancing cybersecurity on low-powered edge devices. This approach involves the deployment of lightweight machine learning models directly onto edge devices to analyze local data streams, such as …

arxiv attack attack surface context cs.ai cs.cr cs.lg decentralized deployment devices distributed edge edge devices feature intelligence language language models large large language model llms machine machine learning proliferation techniques the edge threat threat intelligence