A Study on Transfer Learning TinyML-Based Intrusion Detection Framework on IoT Devices
Jedidah Mwaura, Shunsuke Araki, Muhammad Bisri Musthafa, Samsul Huda, Yasuyuki Nogami · 2025
Currently, there are many ongoing attempts to incorporate transfer learning into Intrusion Detection Systems. Transfer learning leverages on the knowledge of previous models thus reducing training time, improving accuracy and making it possible to train new models with limited data. However, it is difficult to incorporate such a technique into IoT devices with limited computational power. In this paper, we propose a Transfer Learning TinyML-based IDS framework with feature selection to be implemented on a microcontroller for detecting attacks on IoT devices. To implement the proposal, we leveraged knowledge achieved from our previous DDoS attack detection model to create a new transfer learning framework that could detect various IoT attacks such as malware attacks and zero-day attacks. To test the transfer learning framework, we trained it using two separate sets of datasets each containing different attacks. After each training, we evaluated the original model, converted it into a format compatible with a microcontroller and evaluated it further. The TL TinyML-based framework was evaluated based on its accuracy, model sizes and average inference times. As the results confirmed, the framework performed well and can successfully be implemented on a microcontroller without depleting its resources.