A Lightweight Intrusion Detection System for IoT Based on Deep Transfer Learning at the Edge

This paper aims to tackle the cyber threats posed by decentralized settings, such as edge computing, by exploiting the Transfer Learning (TL) approach to create a lightweight Intrusion Detection System (IDS) for resource-constrained IoT devices. The proposed approach shifts the training complexity to the edge, allowing resource-constrained IoT devices to use less resources because they receive from the edge a single (unified) pre-trained supermodel. This supermodel ensures realtime adaptability across multiple datasets and edge environments and achieves 99% accuracy, precision, recall, and F1-score, while reducing latency and increasing scalability. Beyond TL, the proposed approach combines BERT-based semantic feature extraction, MLP classification, and SMOTE class imbalance compensation for improved performance.