tuzsutТруды учебных заведений связиProceedings of Telecommunication Universities1813-324X2712-8830СПбГУТ10.31854/1813-324X-2019-5-3-58-65tuzsut-87Research ArticleИНФОРМАТИКА, ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА И УПРАВЛЕНИЕINFORMATICS, COMPUTER ENGINEERING AND MANAGEMENTМетодика многоаспектной оценки и категоризации вредоносных информационных объектов в сети ИнтернетThe Technique of Multi-aspect Evaluation and Categorization of Malicious Information Objects on the InternetБраницкийА. А.BranitskiyA. ..alexander.branitskiy@gmail.comСаенкоИ. Б.SaenkoI. ..noemail@neicon.ruСанкт-Петербургский институт информатики и автоматизации Российской академии наукРоссияSaint-Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesRussian Federation201913042021535865Copyright © Браницкий А.А., Саенко И.Б., 20212021Браницкий А.А., Саенко И.Б.Branitskiy A..., Saenko I...This work is licensed under a Creative Commons Attribution 4.0 License.https://tuzs.sut.ru/jour/article/view/87

В условиях быстрого развития информационных технологий возникает задача, связанная с обнаружением источников вредоносной информации в сети Интернет. Для ее решения могут применяться методы машинного обучения как один из наиболее популярных и мощных инструментов, предназначенных для выявления зависимостей между входными (наблюдаемыми) данными и выходными (желаемыми) результатами. В данной статье представлена методика, направленная на многоуровневую обработку входных данных о вредоносных информационных объектах в сети Интернет и обеспечивающая их многоаспектную оценку и категоризацию с использованием методов машинного обучения. Цель исследования заключается в повышении эффективности процесса обнаружения вредоносной информации в сети Интернет на примере задачи классификации веб-страниц.

Under the influence of rapid development in the sphere of information technologies, rises the challenge related to detection of malicious information sources on the Internet. To solve this we can use machine learning methods as one of the most popular and powerful tools designed to identify dependencies between input (observed) data and output (desired) results. This article presents a methodology which is aimed at multi-level processing of input data about malicious information objects on the Internet and providing their multi-aspect assessment and categorization using machine learning methods. The purpose of the investigation is to improve the efficiency of the detecting process of malicious information on the Internet using the examples of Web-pages classification.

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The authors declare that there are no conflicts of interest present.