Hybrid Cross-Temporal Contrastive Model with Spiking Energy-Efficient Network Intrusion Detection in IOMT

The Internet of Medical Things (IoMT), a key application of the Internet of Things (IoT), has played a key role, especially during the Covid 19 pandemic. Real-time patient monitoring and remote diagnostics help improve medical services, but this increases the mammoth size of network traffic, which impacts the security quite a bit. However, traditional intrusion detection systems lack synchronization between accuracy and energy efficiency in resource-constrained IoMT environments. To address this issue, we present a hybrid cross-temporal contrastive model coupled with a spiking energy-efficient network for intrusion detection. This approach uses contrastive learning to learn temporal dependencies in network traffic and spiking neural networks (SNNs) for energy-efficient computations. We evaluated the model on the WUSTL-EHMS-2020 dataset, which consists of 44 features (35 of them are network flow measurements, and 8 are biometric patient features), as well as the NSL-KDD dataset to perform a comparative validation. Furthermore, the experiment results prove that our proposed model achieves 99.95% accuracy on the WUSTL-EHMS-2020 dataset with an F1 score of 99.89%, precision of 98.23%, and recall of 99.55%, outperforming conventional models. The model attained 98.2% accuracy, 97.6% precision, 98.5% F1 score, and 97.3% recall on the NSL-KDD Dataset. Our approach shows that these results effectively secure IoMT networks at a low computational cost. Finally, the proposed hybrid model can achieve good performance and energy efficiency for intrusion detection in innovative healthcare systems. In future work, efforts will be made to improve the model's generalization property in diverse IoMT environments and minimize the energy consumption of spiking neural networks in real-time applications.