Intrusion Detection Model for Wireless Sensor Networks Based on MC-GRU

Abstract

A crucial line of defense for the security of wireless sensor network (WSN) is intrusion detection. This research offers a new MC-GRU WSN intrusion detection model based on convolutional neural networks (CNN) and gated recurrent unit (GRU) to solve the issues of low detection accuracy and poor real-time detection in existing WSN intrusion detection algorithms. MC-GRU uses multiple convolutions to extract network data traffic features and uses the high-level features output after convolution operations as input parameters of the GRU network, which strengthens the learning of spatial and time series features of traffic data and improves the detection performance of the model. The experiment results based on the WSN-DS dataset show that the overall detection accuracy of the four types of attack of black hole, gray hole, flooding, and scheduling and normal behaviors reaches 99.57%, and it is also better than the existing WSN intrusion detection algorithms in real-time performance and classification ability.