The purpose of this article is to propose an approach to denoting the parameters of the model for assessing the probability of success of a multi-pass social engineering attack of an attacker on a user. These parameters characterize the evaluation of the probability of propagation of social engineering attacks from user to user in one type of interaction. These estimates are related to the intensity of user interaction, information about which is extracted from data obtained from social Media. The article proposes an approach to the conversion of information about the episodes of interaction between users in the social Media Instagram in assessing the probability of the spread of social engineering attack, based on the Khovanov method. The obtained results help produce social network analysis and serve as a basis for the subsequent analysis of possible trajectories of the spread of multi-pass social engineering attacks, allowing the simulation of social engineering attacks and automated calculation of estimates of the success of the attack on different trajectories. The novelty of the research is to the application quantification method to social links in the context of social engineering attacks.

Original languageEnglish
Title of host publicationINTELLIGENT DISTRIBUTED COMPUTING XIII
Editors Kotenko, C Badica, Desnitsky, D ElBaz, M Ivanovic
PublisherSpringer Nature
Pages272-277
Number of pages6
ISBN (Print)9783030322571
DOIs
StatePublished - 1 Jan 2020
Event13th International Symposium on Intelligent Distributed Computing, IDC 2019 - St. Petersburg, Russian Federation
Duration: 7 Oct 20199 Oct 2019

Publication series

NameStudies in Computational Intelligence
PublisherSPRINGER INTERNATIONAL PUBLISHING AG
Volume868
ISSN (Print)1860-949X

Conference

Conference13th International Symposium on Intelligent Distributed Computing, IDC 2019
Country/TerritoryRussian Federation
CitySt. Petersburg
Period7/10/199/10/19

    Research areas

  • Social engineering attacks, Soft estimates, Soft social computing

    Scopus subject areas

  • Artificial Intelligence

ID: 62789115