IOT threats detection using few shots learning - Chua, Cheng Han (2024)
Existing IoT threat detection methods lack robustness due to the diverse array of potential attack vectors. Currently, most methods are trained and tested using simulated datasets and do not perform well with unseen samples in real-world applications. In this project, we propose a novel few short learning leveraging Large Language Models (LLMs) to improve model robustness in IoT threat detection. Firstly, we develop two specialized LLM models: a text classification model based on DistilBERT and the few shots learning model using Sentence Transformer Fine-Tuning model (SetFit) framework. The DistilBERT threats detection model method performed well with an accuracy of 99.998% due to better semantics and contextual understanding as compared to existing flow statistical analysis. The few-shot learning model demonstrated remarkable performance with an accuracy of 0.89%, despite being trained on a limited amount of data. For unseen samples, we designed a few-shot retraining (FSR) methodology to adapt and learn new attack vectors across multiple variants using transfer learning. The experimental results showed a 90% improvement in accuracy on unseen threats when implemented in a real-world NIDS.