A Comprehensive Review of Deep Learning Techniques for Intrusion Detection in the Internet of Medical Things - Muhammad, Aisha Essa; Abdulrahman, Amer Abdulmajeed (Yayasan Riset dan Pengembangan Intelektual (YRPI), 2025)
The work revisits the security issues of Internet of Medical Things (IoMT) platforms and provides a list of deep learning models used for intrusion detection. The study fills the salient gap in early detection of actual IoMT system intrusions for enhanced medical device and data security. A wide-ranging and systematic investigation of deep learning models, such as CNNs, LSTMs, and hybrid ones (GNNs and BiLSTMs) recently introduced was carried out. These were then analyzed against well-known benchmark datasets, such as ToN-IoT and IoT-Healthcare Security and WUSTL-EHMS-2020, to consider the quality of their detection work on cybersecurity threats for IoMT systems. The results indicated high accuracy in cyber threat detection, reaching even 100% accuracy. But the challenges are still how to decrease false positives and improve the real-time performance of the model on robustness and generalization when making real-world applications. The research is literature-based and aimed to provide some further updates on a secure IoMT framework by identifying recent studies in the security of the IoMT ecosystem and shedding light on future work using hybrid methods, blockchain technology, or federated learning approaches that can contribute to the detection of IDSs. And all can help pave the way for a more secure, privacy-protecting IoMT that safeguards extremely sensitive medical data. The research also enhances the model: utilizing 15+ deep-learning models to propose an IoMT-resistant architecture. This can promote participation in the theoretical research and practical security protocols in the IoMT context, thus drawing attention and comprehension from researchers and practitioners to enhance security protocols.