Classification of cyber attacks in IoMT networks using deep learning: a comparative study - Rumee, Asif Rahman (Journal of Computer Sciences Institute, 2025)
The Internet of Medical Things (IoMT) is transforming healthcare through enhanced remote monitoring and real-time data exchange, but it also introduces significant cybersecurity challenges. This study evaluates various deep learning architectures - Feedforward Neural Networks (FNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Long Short-Term Memory networks (LSTM), and Bi-LSTM - for classifying cyber-attacks in IoMT networks. Utilizing the ECU-IoHT dataset, the Bi-LSTM and CNN models demonstrated superior performance, achieving 60% and 60% accuracy, 75% and 86% precision, 60% and 60% recall, and 63% and 61% F1-score, respectively. These results highlight the effectiveness of Bi-LSTM and CNN in enhancing cybersecurity measures within the IoMT, underscoring their potential to safeguard connected medical devices.