Abstract: A machine learning-based harmful-website classification method performed by a main server includes performing tokenization, by the main server, by extracting and preprocessing an HTML source code of a website by accessing to the website, vectorizing each token according to a preset algorithm, inputting each vector value is input into a machine learning model, and determining whether the website is a harmful website.

Abstract: A harmful-website classification method performed by a main server includes selecting, by the main server, an accessible Internet website among a plurality of Internet websites stored in a database, performing preprocessing of extracting an HTML source code of the accessible Internet website, classifying and tokenizing at least one of a domain name of a website, an address of an image file in the website, a link in the website, and an HTML source for a text in the website, and analyzing each token to determine whether the website is a harmful website.

Abstract: The present disclosure a method of providing identification code insertion service for tracking a duplicated image, which is performed by a server, including: (a) receiving an image from a user terminal; (b) converting the received image to black and white, and selecting a plurality of insertion regions in the converted image; (c) transforming an image of at least one of a plurality of insertion regions selected at random; and (d) mapping an identification code and image information included in the transformed image of the insertion region, storing the identification code and the image information in a database, and providing the image in which the identification code is inserted to the user terminal.

Abstract: The present disclosure a method of providing identification code insertion service for tracking a duplicated image, which is performed by a server, including: (a) receiving an image from a user terminal; (b) converting the received image to black and white, and selecting a plurality of insertion regions in the converted image; (c) transforming an image of at least one of a plurality of insertion regions selected at random; and (d) mapping an identification code and image information included in the transformed image of the insertion region, storing the identification code and the image information in a database, and providing the image in which the identification code is inserted to the user terminal.