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Data Processing, Detection and Protection Algorithms to Minimize the Impact of Malware and Phishing Attacks on Users of Digital Platforms.

Bibliographic Details
Title: Data Processing, Detection and Protection Algorithms to Minimize the Impact of Malware and Phishing Attacks on Users of Digital Platforms.
Authors: Volokitina, T. S., Tanygin, M. O.
Source: Automatic Documentation & Mathematical Linguistics; 2026 Suppl 1, Vol. 60, pS74-S80, 7p
Subject Terms: Malware, Phishing prevention, Machine learning, Data protection, Electronic data processing, Digital technology, Internet security
Abstract: This article is devoted to the development of a scientific and methodological apparatus for improving the effectiveness of protecting digital platforms from cyber threats by creating processing and detection algorithms that take into account the cognitive characteristics of users. A conceptual model of a three-stage protection system is proposed, integrating technical security mechanisms with cognitive decision-making models. A heuristic detection algorithm based on random forest machine learning with analysis of 47 features, including technical URL characteristics and cognitive–semantic content characteristics, has been developed. A methodology for dynamic integration of four threat data sources has been created, reducing response time from 12–14 to 2 h. An algorithm for recursive analysis of redirection chains up to ten levels deep to detect masked threats is proposed. Experimental validation on an empirical base of approximately 1 million records confirmed detection accuracy of 87% when processing 100 thousand records per h. The developed solutions ensure compliance with the requirements of GOST R 57580.1-2017 and Russian legislation in the field of personal data protection. [ABSTRACT FROM AUTHOR]
Copyright of Automatic Documentation & Mathematical Linguistics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
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  Data: Data Processing, Detection and Protection Algorithms to Minimize the Impact of Malware and Phishing Attacks on Users of Digital Platforms.
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  Data: <searchLink fieldCode="AR" term="%22Volokitina%2C+T%2E+S%2E%22">Volokitina, T. S.</searchLink><br /><searchLink fieldCode="AR" term="%22Tanygin%2C+M%2E+O%2E%22">Tanygin, M. O.</searchLink>
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  Data: Automatic Documentation & Mathematical Linguistics; 2026 Suppl 1, Vol. 60, pS74-S80, 7p
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  Data: <searchLink fieldCode="DE" term="%22Malware%22">Malware</searchLink><br /><searchLink fieldCode="DE" term="%22Phishing+prevention%22">Phishing prevention</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Data+protection%22">Data protection</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink>
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  Data: This article is devoted to the development of a scientific and methodological apparatus for improving the effectiveness of protecting digital platforms from cyber threats by creating processing and detection algorithms that take into account the cognitive characteristics of users. A conceptual model of a three-stage protection system is proposed, integrating technical security mechanisms with cognitive decision-making models. A heuristic detection algorithm based on random forest machine learning with analysis of 47 features, including technical URL characteristics and cognitive–semantic content characteristics, has been developed. A methodology for dynamic integration of four threat data sources has been created, reducing response time from 12–14 to 2 h. An algorithm for recursive analysis of redirection chains up to ten levels deep to detect masked threats is proposed. Experimental validation on an empirical base of approximately 1 million records confirmed detection accuracy of 87% when processing 100 thousand records per h. The developed solutions ensure compliance with the requirements of GOST R 57580.1-2017 and Russian legislation in the field of personal data protection. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Automatic Documentation & Mathematical Linguistics is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Malware
        Type: general
      – SubjectFull: Phishing prevention
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      – SubjectFull: Machine learning
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              Text: 2026 Suppl 1
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