According to the method, abnormal behavior detection of false data injection is carried out on data with changed message quantity, a vehicle-mounted network false data injection abnormal data set is constructed, and deep learning is carried out on difference characteristics of abnormal data with labels in a vehicle-mounted network. Collected vehicle-mounted network false data injection abnormal data sets are detected through a CNN classification algorithm, and the method mainly comprises three stages of data preprocessing, model framework design and data testing. In order to solve the problems of high false alarm rate caused by complex and difficult-to-identify behaviors and high missing report rate caused by simple and easy-to-identify behaviors in the detection process, a vehicle-mounted network abnormal behavior detection algorithm based on distance feature and CNN network fusion and a vehicle-mounted network abnormal behavior detection method based on timestamp feature and a classification learning algorithm are provided respectively. A more accurate method is provided for improving the abnormal behavior detection performance of the vehicle-mounted network.
Vehicle-mounted network false information injection abnormal behavior detection method