Abstract: Provided is a method for deciphering obfuscated text for cyber security and an apparatus for the same. The method according to some embodiments includes: converting text including a target character string into an image; recognizing a character string in the image using a text recognition model; and determining that the target character string is an obfuscated character string, based on a similarity between the target character string and the recognized character string being equal to or less than a first reference value.

Abstract: Disclosed is a method for preventing an attack on an IoT network. The method may further include: generating attack situation information of an attack on a network by inputting one or more packet data for the IoT network into an attack discrimination unit; generating, by an attack analysis unit, main feature information for the attack based on the attack situation information; and generating, by an attack prevention unit, attack prevention information based on the main feature information, in which the attack discrimination unit may be an artificial neural network model of a modified autoencoder structure including a softmax layer.

Abstract: 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.

Abstract: Disclosed is a method of detecting anomaly of an unmanned moving object by using a message ID sequence, and the method includes: collecting packet data generated in the unmanned moving object; pre-processing the collected packet data; and detecting an anomaly of the unmanned moving object based on a message ID pattern of the packet data by inputting the pre-processed packet data to a pre-trained neural network model.