Generative adversarial network model and training method to generate message ID sequence on unmanned moving objects

A non-transitory computer readable medium storing a computer program, wherein the computer program performs the following method for training an anomaly detection model of an unmanned moving object when executed by one or more processors, the method comprising:

• collecting packet data generated from the unmanned moving object;
• pre-processing the collected packet data;
• generating a second message identification (“ID”) sequence having a message ID pattern corresponding to a first message ID sequence of the packet data by inputting the pre-processed packet data to a language model to produce converted data for the first message ID sequence;
• generating a third message ID sequence having statistical characteristics corresponding to the first message ID sequence of the packet data by inputting the pre-processed packet data to a first neural network model to produce a fourth message ID sequence;
• training and evaluating the first neural network model by inputting the converted data of the language model and the fourth message ID sequence of the first neural network model to a training model;
• and
• training a second neural network model in a way of predicting a symptom of the unmanned moving object, the training comprising:
• pre-processing packet data generated in a communication state of the unmanned moving object;
• and
• training the second neural network model to predict a message ID following a current message ID sequence of the packet data by inputting the pre-processed packet data to the second neural network model.

A computing device for providing a method of training an anomaly detection model of an unmanned moving object, the computing device comprising a processor including one or more cores;

• and a memory, wherein the processor is configured to:
• collect packet data generated from the unmanned moving object;
• preprocess the collected packet data;
• generate a second message identification (“ID”) sequence having a message ID pattern corresponding to a first message ID sequence of the packet data by inputting the pre-processed packet data to a language model to produce converted data for the first message ID sequence;
• generate a third message ID sequence having statistical characteristics corresponding to the first message ID sequence of the packet data by inputting the pre-processed packet data to a first neural network model to produce a fourth message ID sequence;
• train and evaluate the first neural network model by inputting the converted data of the language model and the fourth message ID sequence of the first neural network model to a training model;
• and
• train a second neural network model in a way of predicting a symptom of the unmanned moving object, the training comprising:
• pre-processing packet data generated in a communication state of the unmanned moving object;
• and
• training the second neural network model to predict a message ID following a current message ID sequence of the packet data by inputting the pre-processed packet data to the second neural network model.