Abstract
Intrusion detection is critical for network security, with deep learning-based algorithms gaining traction. This project introduces NIDS - CNNLSTM, a model designed for the Industrial Internet of Things wireless sensing environment. It effectively identifies network traffic data, ensuring Industrial Internet of Things equipment and operations remain secure. Trained on the NSL_KDD data set, it exhibits strong convergence and performance across three data set, accurately classifying traffic types. Comparative analysis underscores NIDS - CNNLSTM efficacy enhancements. Experimental results validate increased detection rates, classification accuracy, and reduced false alarms, making it suitable for Industrial Internet of Things varied network data scenarios.
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