Disclosed is a method for training an anomaly detection model of an unmanned moving object utilizing a message ID sequence, which includes: collecting packet data generated from the unmanned moving object; pre-processing the collected packet data; generating converted data for a message ID sequence of the packet data by inputting the preprocessed packet data into a language model; generating similar data similar to the message ID sequence of the packet data by inputting the preprocessed packet data into a first neural network model; training and evaluating the first neural network model by inputting the converted data of the language model and the similar data of the first neural network model into a training model; and predicting and training a symptom of the unmanned moving object by inputting the preprocessed packet data and the similar data of the first neural network model into a second neural network model.
GENERATIVE ADVERSARIAL NETWORK MODEL AND TRAINING METHOD TO GENERATE MESSAGE ID SEQUENCE ON UNMANNED MOVING OBJECTS