Abstract: The present disclosure belongs to the technical field of Internet of vehicles (IoV) security and provides an IoV intrusion detection method and device based on an improved convolutional neural network. The method of the present disclosure includes: collecting original data of data traffic during IoV communication, and inputting the original data to a data dimension reduction algorithm model for IoV intrusion detection for preprocessing to obtain standardized data for IoV data analysis; inputting the standardized data for IoV data analysis to an improved convolutional neural network model for calculation, including: performing convolutional calculation and nonlinear activation on the input data for layering; performing two convolutional operations, two pooling operations and one full connection operation on each layer of data; and classifying a data set output by the improved convolutional neural network model through a SoftMax layer.

Abstract: An intrusion detection method and system for Internet of Vehicles based on Spark and combined deep learning are provided. The method includes the following steps: S1: setting up Spark distributed cluster; S2: initializing the Spark distributed cluster, constructing a convolutional neural network (CNN) and long short-term memory (LSTM) combined deep learning algorithm model, initializing parameters, and uploading collected data to a Hadoop distributed file system (HDFS); S3: reading the data from the HDFS for processing, and inputting the data to the CNN-LSTM combined deep learning algorithm model, for recognizing the data; and S4: dividing the data into multiple resilient distributed datasets (RDDs) for batch training with a preset number of iterations.

Abstract: An intrusion detection method and system for Internet of Vehicles based on Spark and combined deep learning are provided. The method includes the following steps: S1: setting up Spark distributed cluster; S2: initializing the Spark distributed cluster, constructing a convolutional neural network (CNN) and long short-term memory (LSTM) combined deep learning algorithm model, initializing parameters, and uploading collected data to a Hadoop distributed file system (HDFS); S3: reading the data from the HDFS for processing, and inputting the data to the CNN-LSTM combined deep learning algorithm model, for recognizing the data; and S4: dividing the data into multiple resilient distributed datasets (RDDs) for batch training with a preset number of iterations.