Work

Analysis of Information Search Methods and Intellectual Classification of Information From the Darknet

Year: 2024

Type: article

Abstract: This paper analyzes existing methods for information retrieval and intelligent classification of data available in closed darknet networks. It considers the features of text data specific to the darknet, such as the presence of specialized slang, jargon, and code expressions. A comparative analysis of traditional search methods and modern approaches based on neural networks, including the BERT model and its adaptation for darknet search, is presented. The challenges associated with the availability and indexing of information are described, and classification methods aimed at improving the accuracy of identifying and filtering illegal content are proposed. The article emphasizes the importance of using machine learning to automate darknet search and monitoring, which can contribute to improving information security.

Source: Вестник КазАТК

DOI: 10.52167/1609-1817-2024-135-6-214-222

Authors: Assel Khassan, Assem Shayakhmetova, Nurgul Baitemirova, Valentina Makhatova, Dariha Ryspaeva

Institutions: L. N. Gumilyov Eurasian National University, Al-Farabi Kazakh National University, Khalel Dosmukhamedov Atyrau University, Astana International University

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4407374262

FWCI:

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