A MODEL FOR COUNTERING PRETEXTING ATTACKS IN SOCIAL NETWORKS BASED ON THE ANALYSIS OF THE STRUCTURE OF SOCIAL ENGINEERING ATTACKS

The Information Security Doctrine of the Russian Federation identifies computer fraud as a key cyber threat in the digital domain. With over 100 million Russian internet users actively engaged on social networks, these platforms have become the primary medium for online communication in the country. Social networks reach an audience of more than 100 million Russian internet users, thus becoming the main means of communication for Russians on the World Wide Web. It allows social networks to serve as one of the most attractive platforms for carrying out fraudulent activities, including using social engineering attacks. Among the social engineering methods used by attackers, pretexting stands out as a particularly prevalent. To effectively mitigate pretexting-based social engineering attacks on social networks, there arises a need to create models for combatting such threats. The urgency of this task stems from: the ubiquity of social media platforms, the rising incidence of cybercrimes leveraging information and communication technologies and the limited body of research focusing specifically on pretexting within social networking environments. The article aims to describe the author’s model for countering pretexting attacks in social networks, grounded in the typical structure of social engineering assaults and to consider fields of its potential applications within methodological frameworks for combating such threats. The proposed model serves as a foundational tool for formulating targeted countermeasures against social engineering attacks of pretexting in social media contexts

Authors

Publication Details

Journal
RSUH/RGGU Bulletin Series Information Science Information Security Mathematics
Published
2025-01-01
DOI
https://doi.org/10.28995/2686-679x-2025-3-54-69
Primary Topic
Scientific Research and Philosophical Inquiry
Type
article
Field-Weighted Citation Impact
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article

A MODEL FOR COUNTERING PRETEXTING ATTACKS IN SOCIAL NETWORKS BASED ON THE ANALYSIS OF THE STRUCTURE OF SOCIAL ENGINEERING ATTACKS

Aleksandr Privalov, Valerii K. Markelov
RSUH/RGGU Bulletin Series Information Science Information Security Mathematics
Scientific Research and Philosophical Inquiry
article

A MODEL FOR COUNTERING PRETEXTING ATTACKS IN SOCIAL NETWORKS BASED ON THE ANALYSIS OF THE STRUCTURE OF SOCIAL ENGINEERING ATTACKS

Aleksandr Privalov, Valerii K. Markelov
article en

Abstract

The Information Security Doctrine of the Russian Federation identifies computer fraud as a key cyber threat in the digital domain. With over 100 million Russian internet users actively engaged on social networks, these platforms have become the primary medium for online communication in the country. Social networks reach an audience of more than 100 million Russian internet users, thus becoming the main means of communication for Russians on the World Wide Web. It allows social networks to serve as one of the most attractive platforms for carrying out fraudulent activities, including using social engineering attacks. Among the social engineering methods used by attackers, pretexting stands out as a particularly prevalent. To effectively mitigate pretexting-based social engineering attacks on social networks, there arises a need to create models for combatting such threats. The urgency of this task stems from: the ubiquity of social media platforms, the rising incidence of cybercrimes leveraging information and communication technologies and the limited body of research focusing specifically on pretexting within social networking environments. The article aims to describe the author’s model for countering pretexting attacks in social networks, grounded in the typical structure of social engineering assaults and to consider fields of its potential applications within methodological frameworks for combating such threats. The proposed model serves as a foundational tool for formulating targeted countermeasures against social engineering attacks of pretexting in social media contexts

RSUH/RGGU Bulletin Series Information Science Information Security Mathematics(3)
Openalex Percentile: Top 61%
Scientific Research and Philosophical Inquiry
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