LLM-Integrated Anomaly Detection for IoT Networks: Framework Structure
TL;DR
A machine learning-based framework to tackle issues in traditional systems in traditional systems is introduced by combining large language models (LLMs) and is effective in identifying possible threats as well as filling the semantic gap.
Abstract
Internet of Things (IoT) devices are vulnerable to zero-day attacks because most of them have weak or no inherent security due to the resource constraints of the devices. This weakness underscores the growing need for anomaly-based intrusion detection systems tailored to IoT networks. Nevertheless, general anomaly detection traditionally has a high number of false positives that drain analysts' time. Also, a semantic difference exists between the system's results and the operators' interpretations. We introduce a machine learning-based framework to tackle these issues in traditional systems in this paper by combining large language models (LLMs). Our model is effective in identifying possible threats as well as filling the semantic gap. The framework uses isolation forests to detect anomalies and random forests to measure device integrity. To further improve the assessment of anomalies and increase interpretability, system insights are further refined using GPT-4o mini, an LLM. The model gives statistical summaries of the IoT traffic, a risk score, and an explanation in easy language, which is easy to understand and therefore makes the process of decision-making easier. Such a novel system reduces the reliance on dedicated network operators and allows non-technical users to better understand and act on the results of the system.