title: Ð¡Ð¸ÑÑÐµÐ¼Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð½Ð¾Ð³Ð¾ Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ñ ÑÐ¸ÑÐ¸Ð½Ð³Ð¾Ð²ÑÑ ÑÐ°Ð¹ÑÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: The system for automated detection of phishing sites
Creators: name: Ð ÑÐ±Ð°Ð»ÑÑÐµÐ½ÐºÐ¾, Ð¡ÐµÑÐ³ÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2019
Subjects: ÑÐ¸ÑÐ¸Ð½Ð³
ÐºÐ¸Ð±ÐµÑÑÐºÐ²Ð¾ÑÑÐ¸Ð½Ð³
ÐºÑÐµÐ´Ð¸ÑÐ½Ð°Ñ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸Ñ
Ð´Ð¾Ð¼ÐµÐ½Ð½Ð¾Ðµ Ð¸Ð¼Ñ
ÑÐ°ÑÑÑÐ¾ÑÐ½Ð¸Ðµ ÐÐµÐ²ÐµÐ½ÑÑÐµÐ¹Ð½Ð°
Ð»ÐµÐºÑÐ¸ÑÐµÑÐºÐ°Ñ ÑÐ²ÑÐ·Ñ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
OpenCV
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ
Ð½Ð°ÑÐºÐ° Ð¾ Ð´Ð°Ð½Ð½ÑÑ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÑÐ²ÐµÑÑÐ¾ÑÐ½ÑÐµ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
FastCNN
phishing
cybersquatting
credit institution
domain name
Levenshtein distance
lexical communication
computer vision
machine vision
recognition
data science
machine learning
neural networks
convolutional neural networks
deep learning

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description: This paper is devoted to creating a system for automated detection of phishing sites. The relevance of the work lies in the fact that, at the time of publication of this work, existing methods for detecting phishing resources become obsolete, while advanced methods of machine learning are developing and achieve high accuracy sufficient to solve applied problems. The analysis of the main methods of phishing, as well as algorithms for their recognition: HTML-page parsing, page analysis by computer vision methods, as well as page analysis using a convolutional neural network. A software tool has been developed that allows online detection of phishing and other suspicious sites. Based on the simulation and implementation of the presented algorithms, the results of the system were evaluated. Also conducted a comparative study between the algorithms for pattern recognition on the page.
Chapter 1 contains a classification of phishing resources, an overview of the algorithm for obtaining data, the main criteria for suspecting phishing, and the social significance of identifying phishing. Chapter 2 presents algorithms for recognizing phishing sites: analyzing the textual content of pages, the basics of recognizing objects in an image, a basic understanding of the basics of machine learning, the structure of convolutional neural networks, and describes the main methods that are involved in the study. Chapter 3 describes the ongoing comparative study. Conclusions are drawn in the chapter.