STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent.

The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. The STID-Net is also experimented with the MBGD and SGD optimizers, and we are satisfied with the performance of the SGD optimizer in both the IoMT and IIoT datasets. The SGD optimized model provides a faster convergence and better weight adjustments for handling noisy datasets, making it robust and scalable for diverse IoT applications. This experimental work demonstrates an accuracy of 97.14% and 97.85% with the MBGD optimizer in the IoMT and IIoT datasets, while it attained 98.58% and 99.15% with SGD optimization, respectively. The proposed methodology also outperforms the standalone CNN and LSTM models incorporated with both optimizers, and the result indicates the robustness and scalability of STID-Net in medical and industrial applications.