Abstract: Provided is a deep learning method including a step of each of at least two or more deep learning machines learning a web traffic by using a hexadecimal; a step of the deep learning machines learning the web traffic by using an incremental learning using a weight; a step of, when the web traffic is received, each of the deep learning machines encoding a character string of the web traffic with UTF-8 hexadecimal; a step of each of the deep learning machines converting the character string into an image and deep learning the image.

Abstract: A method of judging reliability of a deep learning machine, includes: temporarily judging a class of data to be judged; checking an attack/normal ratio of temporarily judged data, configuring N mini-batches by using M test data that have been judged whether it is normal or attack data, and configuring T mini-batch sets each including the N mini-batches; and iteratively performing multiple times a process of judging the test data provided for each of the N mini-batches configuring the mini-batch sets to judge an attack/normal ratio of each of the N mini-batches, wherein the M test data that are used for each of the T mini-batch sets are the same but combinations of the test data of each of the mini-batches are different for each of the mini-batches and a size of each of the mini-batches is M/N.

Abstract: Provided is a deep learning method including a step of each of at least two or more deep learning machines learning a web traffic by using a hexadecimal; a step of the deep learning machines learning the web traffic by using an incremental learning using a weight; a step of, when the web traffic is received, each of the deep learning machines encoding a character string of the web traffic with UTF-8 hexadecimal; a step of each of the deep learning machines converting the character string into an image and deep learning the image.