Deep Learning-Based Intrusion Detection Model for Internet of Things Environment

The proposed IoT intrusion detection model uses machine learning to compensate for data loss in convolutional neural networks' initial pooling layer and the gradient vanishing problem that recurrent neural networks face when processing long sequential input. Time series data for intrusion detection models is optimized using Gramme matrix field visualization. To reduce data loss, the SoftPool pooling layer replaced the original. The recurrent neural network uses a bidirectional gated recurrent unit with fewer parameters and a multi-head structure modeled after the self-attention mechanism. Baseline data will be TON-IoT data. The number of heads multi-head bidirectional gated recurrent unit, pooling layer topologies, and visualization approaches are experimental parameters. The simulation results show that a three-head structure, SoftPool for layer pooling, and for accuracy, precision, detection rate, F1 score, and false positive rate, multi-class classification works well with field and Gramme matrix visualization. The suggested model outperforms state-of-the-art models in accuracy, detection rate, F1 score, and false positive rate by 0.14, 0.09, 0.13, and 0.08. Modern machine learning improves IoT intrusion detection.