  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
resource-constrained edge devices. This approach involves the deployment of
lightweight machine learning models directly onto edge devices to analyze local
data streams, such as network traffic and system logs, in real-time.
Additionally, distributing computational tasks to an edge server reduces
latency and improves responsiveness while also enhancing privacy by processing
sensitive data locally. LLM servers can enable these edge servers to
autonomously adapt to evolving threats and attack patterns, continuously
updating their models to improve detection accuracy and reduce false positives.
Furthermore, collaborative learning mechanisms facilitate peer-to-peer secure
and trustworthy knowledge sharing among edge devices, enhancing the collective
intelligence of the network and enabling dynamic threat mitigation measures
such as device quarantine in response to detected anomalies. The scalability
and flexibility of this approach make it well-suited for diverse and evolving
network environments, as edge devices only send suspicious information such as
network traffic and system log changes, offering a resilient and efficient
solution to combat emerging cyber threats at the network edge. Thus, our
proposed framework can improve edge computing security by providing better
security in cyber threat detection and mitigation by isolating the edge devices
from the network.
