One of the measures to prevent multi-pass social engineering attacks is to identify the chains of user, which are most susceptible to such attacks. The aim of the study is to combine a mathematical model for estimating the probability of success of the propagation of a multi-pass social engineering attack between users with a model for calculating information influence. Namely, it is proposed to include in estimating the intensity of interactions between users (which used in the model of the propagation of a multi-pass social engineering attack) estimating of power of influence actions of agents. The scientific significance of the work consists in the development of a mathematical structure for modeling the actions of an attacker-social engineer and creating a foundation for the subsequent analysis of the social graph of the organization's employees. The practical significance lies in the formation of opportunities for decision-makers. Therefore, they will be able to take more precise measures for increase the level of security as individual employees as the organization generally.

Original languageEnglish
Title of host publicationProceedings of 2021 24th International Conference on Soft Computing and Measurements, SCM 2021
EditorsS. Shaposhnikov
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages49-51
Number of pages3
ISBN (Electronic)9781665439749
DOIs
StatePublished - 26 May 2021
Event24th International Conference on Soft Computing and Measurements, SCM 2021 - St. Petersburg, Russian Federation
Duration: 26 May 202128 May 2021

Conference

Conference24th International Conference on Soft Computing and Measurements, SCM 2021
Country/TerritoryRussian Federation
CitySt. Petersburg
Period26/05/2128/05/21

    Research areas

  • intensity of users interaction, model of informational influence, social engineering attacks, social graphs

    Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications
  • Management Science and Operations Research
  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Modelling and Simulation

ID: 87278331