基于洋葱空间网站内容与特征的网站分类

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus) Pub Date : 2020-01-01 DOI:10.1109/EIConRus49466.2020.9039347

Denis Korolev, Alexey A. Frolov, I. Babalova

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引用次数: 1

摘要

本文描述了一种洋葱站点分类方法。根据研究结果，建立了洋葱空间中最具传播性的网站模型。要创建这样的模型，需要使用经过特殊训练的神经网络。神经网络的分类是基于五个不同的类别，如使用认证系统、企业电子邮件、可读URL、反馈和在线类型。对暗网中传播最广的网站类型进行了统计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Classification of Websites Based on the Content and Features of Sites in Onion Space

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.

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来源期刊

2020 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus)

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