Track Signal Intrusion Detection Method Based on Deep Learning in Cloud-Edge Collaborative Computing Environment

Aiming at the low accuracy of the track signal intrusion detection (IDe) algorithm in the traditional cloud-side collaborative computing environment, this paper proposes a deep learning (D-L)-based track signal IDe method in the cloud edge collaborative computing environment. First, the main framework of the IDe method is constructed by comprehensively considering the backbone network, network transmission and ground equipment, and edge computing (EC) is introduced to cloud services. Then, the The CNN (Convolutional Neural Networks)-attention-based BiLSTM (Bi-directional Long Short-Term Memory) neural network is used in the cloud center layer of the system to train the historical data, a D-L method is proposed. Finally, a pooling layer and a dropout layer are introduced into the model to effectively prevent the overfitting of the model and achieve accurate detection of track signal intrusion. The purpose of introducing the pooling layer is to accelerate the model convergence, remove the redundancy and reduce the feature dimension, and the purpose of introducing the dropout layer is to prevent the overfitting of the model. Through simulation experiments, the proposed IDe method and the other three methods are compared and analyzed under the same conditions. The results show that the F1 value of the method proposed in this paper is optimal under four different types of sample data. The F1 value is the lowest of 0.948 and the highest of 0.963. The performance of the algorithm is better than the other three comparison algorithms. The method proposed in this paper is important for solving the IDe signal in the cloud-edge cooperative environment, and also provides a theoretical basis for tracking the signal IDe direction.