Wireless Sensor Network-based Intrusion Detection Technique using Deep Learning Approach of CNN-GRU

In a WSN, sensors are always collecting information and transmitting it to other nodes. Their primary uses are in fields like defense, smart city development, and agricultural monitoring. The WSN needs to perform at a premium for these uses. Yet, there are many potential security concerns that could compromise a WSN's performance. Any interference with the WSN could severely degrade its functionality and lead to catastrophic failures. As a result, having the ability to quickly identify and stop intrusions is crucial. The goal of this proposed is to use a GRU-CNN model for quick detection and prevention of any intrusion. After receiving the input, the proposed method uses normalization and discretization for preprocessing the data, PSO (Particle Swarm Optimization) for feature extraction, CFS for feature selection and finally training the model by CNN-GRU. To better detect intrusions in wireless sensor networks, a GRU-CNN hybrid neural network model was presented; although the CNN module is responsible for extracting the feature vector from other high-dimensional data, the GRU module is responsible for from time sequence data to extract the feature vector.