| Title | Adaptation of the Multi-pass social Engineering Attack Model Taking into Account Informational Influence |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Khlobystova, Anastasiia O., Abramov, Maxim V. |
| Conference Name | 2021 XXIV International Conference on Soft Computing and Measurements (SCM) |
| Keywords | Adaptation models, Analytical models, Bayes methods, Computational modeling, Human Behavior, intensity of users interaction, Mathematical model, model of informational influence, Organizations, pubcrawl, Scalability, security, Social Agents, Social Engineering Attacks, social graphs |
| Abstract | 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. |
| DOI | 10.1109/SCM52931.2021.9507195 |
| Citation Key | khlobystova_adaptation_2021 |
Adaptation of the Multi-pass social Engineering Attack Model Taking into Account Informational Influence