| Title | Classification of Websites Based on the Content and Features of Sites in Onion Space |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Korolev, D., Frolov, A., Babalova, I. |
| Conference Name | 2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) |
| Keywords | analysis of onion sites, categories of onion sites, Dark Net, dark web, Documentation, Electronic mail, feature extraction, features of onion sites, feedback, Human Behavior, human factors, Internet, learning (artificial intelligence), machine learning, neural nets, neural network training, Neural networks, onion site classification, onion space, onion-sites, parsing sites, pattern classification, pubcrawl, python, site content, site features, text analysis methods, Tor, Training, Uniform resource locators, Web sites, website classification |
| Abstract | This paper describes a method for classifying onion sites. According to the results of the research, the most spread model of site in onion space is built. To create such a model, a specially trained neural network is used. The classification of neural network is based on five different categories such as using authentication system, corporate email, readable URL, feedback and type of onion-site. The statistics of the most spread types of websites in Dark Net are given. |
| DOI | 10.1109/EIConRus49466.2020.9039347 |
| Citation Key | korolev_classification_2020 |
Classification of Websites Based on the Content and Features of Sites in Onion Space