Abstract: Disclosed is a method of analyzing abnormal behavior by using data imaging, including: receiving data to be analyzed as an input, wherein the data to be analyzed is related to a state of a system to be analyzed; converting the inputted data to be analyzed into image data; training a neural network unit with the converted image data as an input; and detecting or predicting abnormal behavior in the system to be analyzed, at the neural network unit, which has received the image data converted from the data to be analyzed as the input and completed training.

Abstract: An AI-based malware detection method is provided. The method includes inputting malware binary data, extracting metadata from the inputted malware binary data, converting the extracted metadata into image data, and training a neural network on the converted image data to classify malware. Malware binary data can be effectively classified by converting the binary data to image data and analyzed through deep learning-based image models. In addition, results from the AI detection algorithm technology can be displayed visually for easy interpretation.

Abstract: Disclosed is a method of analyzing abnormal behavior by using data imaging, including: receiving data to be analyzed as an input, wherein the data to be analyzed is related to a state of a system to be analyzed; converting the inputted data to be analyzed into image data; training a neural network unit with the converted image data as an input; and detecting or predicting abnormal behavior in the system to be analyzed, at the neural network unit, which has received the image data converted from the data to be analyzed as the input and completed training.

Abstract: An AI-based malware detection method is disclosed. According to the present disclosure, said method includes inputting malware binary data, extracting metadata from the inputted malware binary data, converting the extracted metadata into image data, and training a neural network on the converted image data to classify malware. According to the present disclosure, malware binary data can be effectively classified by converting said binary data to image data and analyzed through deep learning-based image models. In addition, results from said AI detection algorithm technology can be displayed visually for easy interpretation.