IoT Vulnerability Detection using Featureless LLM CyBert Model

This work aims to leverage large language models (LLMs) and a featureless approach to effectively detect vulnerabilities in Internet of Things (IoT) network traffic. By directly learning from the Ripple20 dataset, a featureless LLM model, CyBERT, was designed that can efficiently distinguish between secure and vulnerable IoT devices without relying on handcrafted features. This LLM-based classifier could be instrumental in identifying IoT networks that pose potential threats to other connected devices by uncovering critical vulnerabilities. The experimental results demonstrate the exceptional capabilities of the featureless CyBERT model, which achieves high accuracy, precision, recall, and F<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score in detecting zero-day vulnerabilities. Moreover, the model significantly outperforms traditional methods in terms of detection speed. These results have profound implications for the future of IoT security, paving the way for real-time threat and attack detection.